{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discretisation with Decision Trees\n",
    "\n",
    "Discretisation is the process of transforming continuous variables into discrete variables by creating a set of contiguous intervals that spans the range of the variable's values. **Supervised discretisation** methods use target information to create the contiguous bins or intervals. Several supervised discretisation methods have been described, see for example the article [Discretisation: An Enabling technique](http://www.public.asu.edu/~huanliu/papers/dmkd02.pdf) for a summary.\n",
    "\n",
    "However, I have only seen the discretisation using decision trees being used both in Data Science competitions and business settings:\n",
    "\n",
    "Discretisation using trees was implemented by the winning solution of the KDD 2009 cup: \"Winning the KDD Cup Orange Challenge with Ensemble Selection\" (http://www.mtome.com/Publications/CiML/CiML-v3-book.pdf).\n",
    "\n",
    "It is also used in a peer to peer lending company in the UK. See this [blog](https://blog.zopa.com/2017/07/20/tips-honing-logistic-regression-models/) for details of the benefit of using  discretisation using decision trees.\n",
    "\n",
    "\n",
    "Discretisation with Decision Trees consists of using a decision tree to identify the optimal splitting points that would determine the bins or contiguous intervals: \n",
    "- First, it trains a decision tree of limited depth (2, 3  or 4) using the variable we want to discretise to predict the target.\n",
    "- The original variable values are then replaced by the probability returned by the tree. The probability is the same for all the observations within a single bin, thus replacing by the probability is equivalent to grouping the observations within the cut-off decided by the decision tree.\n",
    "\n",
    "### Advantages\n",
    "\n",
    "- The probabilistic predictions returned decision tree are monotonically related to the target.\n",
    "- The new bins show decreased entropy, this is the observations within each bucket / bin are more similar to themselves than to those of other buckets / bins.\n",
    "- The tree finds the bins automatically\n",
    "\n",
    "### Disadvantages\n",
    "- It may cause over-fitting\n",
    "- More importantly, some tuning of tree parameters need to be done to obtain the optimal splits (e.g., depth, minimum number of samples in one partition, maximum number of partitions, and a minimum information gain). This it can be time consuming.\n",
    "\n",
    "\n",
    "Below, I will demonstrate how to perform discretisation with decision trees using the Titanic dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Titanic dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the numerical variables of the Titanic Dataset\n",
    "data = pd.read_csv('titanic.csv', usecols = ['Age', 'Fare', 'Survived'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Important:\n",
    "\n",
    "The tree should be built using the training dataset, and then used to replace the same feature in the testing dataset, to avoid over-fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((623, 3), (268, 3))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's separate into train and test set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data[['Age', 'Fare', 'Survived']],\n",
    "                                                    data.Survived, test_size=0.3,\n",
    "                                                    random_state=0)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Missing Data\n",
    "\n",
    "The variable Age contains missing data, that I will fill by extracting a random sample of the variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def impute_na(data, variable):\n",
    "    df = data.copy()\n",
    "    \n",
    "    # random sampling\n",
    "    df[variable+'_random'] = df[variable]\n",
    "    # extract the random sample to fill the na\n",
    "    random_sample = X_train[variable].dropna().sample(df[variable].isnull().sum(), random_state=0)\n",
    "    # pandas needs to have the same index in order to merge datasets\n",
    "    random_sample.index = df[df[variable].isnull()].index\n",
    "    df.loc[df[variable].isnull(), variable+'_random'] = random_sample\n",
    "    \n",
    "    return df[variable+'_random']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train['Age'] = impute_na(data, 'Age')\n",
    "X_test['Age'] = impute_na(data, 'Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>51.0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>49.0</td>\n",
       "      <td>76.7292</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>1.0</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>54.0</td>\n",
       "      <td>77.2875</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>14.5</td>\n",
       "      <td>14.4583</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age     Fare  Survived\n",
       "857  51.0  26.5500         1\n",
       "52   49.0  76.7292         1\n",
       "386   1.0  46.9000         0\n",
       "124  54.0  77.2875         0\n",
       "578  14.5  14.4583         0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age_tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>51.0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>49.0</td>\n",
       "      <td>76.7292</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>1.0</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>54.0</td>\n",
       "      <td>77.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>14.5</td>\n",
       "      <td>14.4583</td>\n",
       "      <td>0</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>8.0</td>\n",
       "      <td>36.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>24.0</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>24.0</td>\n",
       "      <td>7.1417</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age      Fare  Survived  Age_tree\n",
       "857  51.0   26.5500         1  0.370849\n",
       "52   49.0   76.7292         1  0.370849\n",
       "386   1.0   46.9000         0  0.516667\n",
       "124  54.0   77.2875         0  0.370849\n",
       "578  14.5   14.4583         0  0.818182\n",
       "549   8.0   36.7500         1  0.516667\n",
       "118  24.0  247.5208         0  0.370849\n",
       "12   20.0    8.0500         0  0.370849\n",
       "157  30.0    8.0500         0  0.370849\n",
       "127  24.0    7.1417         1  0.370849"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example: build Classification tree using Age to predict Survived\n",
    "\n",
    "tree_model = DecisionTreeClassifier(max_depth=2)\n",
    "tree_model.fit(X_train.Age.to_frame(), X_train.Survived)\n",
    "X_train['Age_tree'] = tree_model.predict_proba(X_train.Age.to_frame())[:,1]\n",
    "X_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.37084871,  0.51666667,  0.81818182,  0.1       ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.Age_tree.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A tree of depth 2, makes 2 splits, therefore generating 4 buckets, that is why we  see 4 different probabilities in the output above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x188b1cc2e8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V0FlE2uMUi3H4nXElIi2AXaqqIjIQZ3fZvlLkNyYspe4+wpS5CXyxYS9dm0fy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\nXpjCe/HpNI2swbPX9uXKfq3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x188a16e470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# monotonic relationship with target\n",
    "\n",
    "fig = plt.figure()\n",
    "fig = X_train.groupby(['Age_tree'])['Survived'].mean().plot()\n",
    "fig.set_title('Monotonic relationship between discretised Age and target')\n",
    "fig.set_ylabel('Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x188b59f9e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TTwd+le2DOvbdbHufBstrnc3h85ke/Qgk/QXVouYCrql/BJwpaW6TtbXQbyW9XdIxgOvT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x188a137908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of passengers per probabilistic bucket / bin\n",
    "\n",
    "X_train.groupby(['Age_tree'])['Survived'].count().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x188b6305c0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6S22fLemDwMdtLwOolyZbP2htz6XA+2yvqhdqPg04GDgV+Pt6DdVZwMXAqyav\nCSKeLYEeXTSXKkChmmxpLtV3+SzbTwP3Sbq0Pr4rsDuwpA7nDRn7cmNnwO//MngdcNagNShfUD8e\nCuw2aP8Wkjaz/egYPyti3BLo0SmStqbqEe8hyVQBbeCckd5CNfHSARP42Mfqxw2Ah2zvNcxrNgD2\nt/2bCXxOxIRkDD265u3AP9t+qe3ZtncEfgH8EvjP9Vj6DOCg+vW3An2SDgCQtLGkVz/H7/8V1SLC\nz2L7EeAXko6uf5ck7VkfvgT40MBrJQ0X+hGTKoEeXTOXZ/fGvw/8EXAPcDPwbeAa4GHbv6P6R+AU\nSdcBK6iGTUbyTeAfBk6KDnP8ncAJ9e+6iT8s5PwXQL+k6yXdDLxvPP9xERORuVyiGANj1pK2oVq8\n4EDb97VdV8RUyRh6lOQ8SVsCmwCfSZjHdJMeekxLkr4CDL1W/FTb32ijnogmJNAjIgqRk6IREYVI\noEdEFCKBHhFRiAR6REQhEugREYX4/4z/Z0Ej1zJ7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x188b698748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# median age within each bucket originated by the tree\n",
    "\n",
    "X_train.groupby(['Age_tree'])['Age'].median().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_tree</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.100000</th>\n",
       "      <td>64.00</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.370849</th>\n",
       "      <td>16.00</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.516667</th>\n",
       "      <td>0.67</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.818182</th>\n",
       "      <td>12.00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Age   Age\n",
       "Age_tree             \n",
       "0.100000  64.00  80.0\n",
       "0.370849  16.00  63.0\n",
       "0.516667   0.67  11.0\n",
       "0.818182  12.00  15.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see the Age limits buckets generated by the tree\n",
    "# by capturing the minimum and maximum age per each probability bucket, \n",
    "# we get an idea of the bucket cut-offs\n",
    "\n",
    "pd.concat( [X_train.groupby(['Age_tree'])['Age'].min(),\n",
    "            X_train.groupby(['Age_tree'])['Age'].max()], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, the decision tree generated the buckets: 0-11, 12-15, 16-63 and 46-80, with probabilities of survival of .51, .81, .37 and .1 respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tree visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# we can go ahead and visualise the tree by saving the model to a file, and opening that file in the below indicated link\n",
    "\n",
    "with open(\"tree_model.txt\", \"w\") as f:\n",
    "    f = export_graphviz(tree_model, out_file=f)\n",
    "\n",
    "#http://webgraphviz.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JI+D6PAidfumMGp/VFut2HTqBIwf3iTopjnfUqzQ2rFEnNlW0b99eVgapHQD9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i3Vou1f8g83d0xJfIz2bJivl555QjW6DTcbAQAAAAD3GwEggAcmLsxXx84c\nMivpypaFcnBw0PIjrmaL5HR6r4JizeI/9MUXX6hatWpmZf9Wr16trFmzytPT02y5NbdjC4zjNHOH\ns9li43ryiC5d8TWrW6tcuog+7TTErAAAAAAA9xsBIIB04/za2UawtHCvk9kihQR6a+hvDY32n7oP\nVIfWTdTqt791eN8qZbO0dR2+0bJXrM4c3KSCWR3UuKttZlmA60m1+L6BVm7fqx1r56tAzqxq3Xey\n0Xc71llwhQsXVteuXc2WR4d1cZBMmTJp8+bNZkvKnA/OUQbLez900U55e3vryoXjGjdiksLMCYRH\n1k5VgUIvas2OfVo+qa9l36xase+yrdOi0rsvXw8AvZ326pvaP2jbvkPasWGRPnyrmk64R1t64jX2\n787qOmiiThw7rKY1SuuZYh/KLSDK+DkAAAAAQOoRAAJIN1IKAK0u75+vxzPl1t4LwWaLRaSX3sqe\nW11HrDcbwlX5zaxq2n2MUX1W6RW1+HusVq1aZWyL58/U2LGz5Bty68tcvby8jABs8eLFZsujx7o4\nyLPPPqsBAwaYLTezBoDW4zR1s3mcYsK0edUahSaZqRkR7K+9O7dp48oFKpw3u/pM3mT2JA8AQ73P\n6d3iT6lQsbc0fo6j0Wa1anQPlXr3M61fu9p2DB1XWI7fWO04etbcAwAAAACQWgSAANIN5w3zjGBp\nyYGLZouN64GFRgC453yQ2WIR6alSWXLqz/HbzYZYffRaFrXoMc6ofqhUXIVL15Z1LlkC59Mn5eGT\nJERMYufOncZznz1LwGR1u8VBXI/OtwWAW5IHtQk6fFVeHzTqZVbX9HrerOo7NeE42S4BrtZlmPE4\nMiLU+F8rl5M7VfiJDOo1Y5Ncd1ufI5OWHnAxey0i/XT4BMcHAAAAAP4tAkAA6cLB3ZvVtWMLValS\nRW1++1PbDp4y2kO93fRPj99UuUpl/dZrkC54+BntVhvnDdNTeZ5QmapfasOO3WpU+01VqNlcxy56\nGf1zR/dWgTzZ5eCQQWU/risn90Cj/UajRo3SE088oZCQELMFVtbFQV566aVki4NYFwHp+Usb4zg1\n79BN2/YcN3sS7V01Wflz59JbFWto9YZN+v6zt/VauWo6fcVH+zYvVZ1qVfXl96208+BZuZxwVIXy\n5dSuXUe1/7GN2v3eT77mtcReTnv0Wbk3jLAx37PFNHLuOqMdAAAAAPDvEAACeKQ1bNhQ77zzjlnh\nRtbFQbJkySIPDw+zBQAAAADwsCEABPDIevvtt9W4cWOzwq0kLA6yaVPiffwAAAAAAA8PAkAAjxzr\npb65c+fWyJEjzRbciXVxkEKFCql///5mCwAAAADgYUEACOCRYl3kw3pPuW3btpkt+DeqVq2qmjVr\nmhUAAAAA4GFAAAjALvn5+cnd3d2sbJYtW6aMGTPe1I5/p1u3bnrxxRcVGxtrtthcvnw52YIhAAAA\nAID0gQAQgF3q1auXMdPPun3yySdq2rSpSpcubfbiXm3evFkFCxZU79699cwzzxjvc4YMGXT+/Hlz\nDwAAAABAekEACMDuREVFqUiRItcDwKRbvnz5NGjQIPn4+Jh7I7Xi4+Pl7OysFi1apPjeWjfr7EAA\nAAAAQPpCAAjA7ixfvjzFcCpha9++vUJDQ829kVrWAHDfvn0qWbJkiu+rdXv++ecVHh5u/gQAAAAA\nID0gAARgd7788subgqk8efJo5cqV5h64VzExMWrbtu1N77N1mzdvnrkXAAAAACA9IAAEYFes96DL\nmjXr9TCqcuXK8vLyMntxPzg6Oipv3rzX33PrSsEAAAAAgPSDABBIJ3bu3KnJkydrzJgxbHe5TZw4\nUTVq1DBCKOtlqtOmTdOECRNS3Pd+bzNnzkzz4DE6OlqLFy/W+PHjU3zOB7lZ3+s+ffoYqyxb3/8e\nPXqky9dpT9vUqVN1/Phxc3QAAAAAwK0RAALpRIUKFTRw4ECzwt1yd3c3Hz1YJUqU0KJFi8wqbVy7\nds1YeffMmTNmS/rFrMv7r379+mrVqpVZAQAAAMCtEQAC6UTFihWNGVR4+Fnvj1e8eHFjtl5aSggA\nDx06ZLbgUfbtt9+qdevWZgUAAAAAt0YACKQTBID2gwAQ/wUCQAAAAACpRQAIpBMPKgCMj49TWFio\nPNzd5e5+VdEx8WZPyuJiIuXpYd3XQyFh4ZafNztw3SMXAFoGQVREuK56eRiXYIeGR5sdtxAXI28P\n274hoaGKYxDdFQJAAAAAAKlFAAikE2kVAMbGxMjl3BFVe/d5YzGG0cv3mz027b+tqLo/9tJVvyAj\nvNszf7gKF35dMWZ/giG/t9DvA2coJCREc4d3U6vfB5k9CUJU9umnNH79UbNOPZdLlxR8p5DoFsKD\ng3Ri3yYVzOKgHzoNNlttoiNCdO7UIb3/Um4Vq9bGbL21+OgQtf2umt5++21j+6BqHTn7hZu9d+9h\nDwDj42Lle/WKfm9uW1Dl21+Hmz02cwd30LtVv9YZF2/FxsUr1OWQCufJo4OuoeYeNhtmDVXdpr/J\nPyhEx7fOV53vmulapNlp+rDUK+o60tGsUs/T3UXeAWFmdRciA/Rzw9qq3+IX7TvupNCIKFljyMhQ\nHw35q4v+6PW3fm3bwPj72/49w/YztxDqfU7Vype6Po7qNO2i8Nj7H2oSAAIAAABILQJAIJ24HzMA\nz26bYwswBs6X9+WTatG2o7xDYs1em32LRqt48bcVZNZWf7f8XAWKfaw4s5biVf2dfKrWNskiJZHe\n+vD5Qpq86farkMZEBGjt4lnq9GsH/fFXf+08ZFvAwv3iIc2dO/eW26a9t/u9karyVnY16TrCrJPr\nUP0DvVWrvVmlLC4yUB+/Xkh5ChVR61976pzbNbPn3tnTDMAI33MqnMtBr1drqUB/X/36Y0vtPedh\n9tqEuh5RiYIFtMvJx2yRdi34xzL2ntJZn8RoeXD7asr/5qeKMmspVh+9U0o9x64261uIj9LuTY76\no8tv6tS1p1Zt3msE1sFuF1IcOwnbmi27jFDvRpN6NJTD4y/rWioyunF/d9BP/aaZ1c2C3I+qUE4H\nFSlVRj36j5Zv6I1R+v1DAAgAAAAgtQgAgXTivl0CHOujF7I46KnXP0kSvCS6OQAMU5USmfVmwy5m\nbdO0VjkVKPZ54kzB2wSAwZ7n9EXpUqrV9Dd5Bt6PQOTeA8CkAr0u6asPSipLvtfkGXzvr9ceLwH+\n4cMX5ZAht057R5gtiVIKAPs2+UAZipZRoFlbzRz2uxwcntZZr4TZn7cOAGPCAtS2zkcq+/E3OuGS\n+Hvv1Yk1k41QvNuIufLw9NBWx+kqWbiwRi3Yae5hFa+tqxeq8rsvG/s26DhQqZmveunENr1SMJNe\n+bj5TTNq7wcCQAAAAACpRQAIpBP3IwDc5zhDv/cZazwe26W+HBwyafUhV6NOkNIMwE7fVdCL735n\nVlZx+vyNPKrasq9ZW6RyBqCv+wVNHvmP2rZrp8EjJumcq7fRHnTNTUeOHLnldt7F09gvZWkbACb4\nplZljVl7xKzunj0FgF7n9urHdr8rJF46vnaSEYh1HZv8kt2UAsA1E/9Qhqwv6UqSq4IHtf1YT75a\n2XL0EqRuBmCo/1UtmjleP/3UTn/1H6KDJy8Y7dFB11IcOwnb2QuXb5oBuHveYDlkyKTTVxMv9b68\nf6Hxd2138jNbkgpQ8QJ51G/aDrO+gyhXVf2wlI643RyUpjUCQAAAAACpRQAIpBNpFwDG68y+dapU\nvpx2Xwow22xOrptgBB3l63aUX6htPmBKAaA1YGtX9zP1m7zG8jheK8b3VJ1mvyWfBZXKAPAm8VE6\nsG+//M3nvzvhevd5B1Vr9ZdZJ9e04it6NlmAaREXo4O7d8jJ9apRblsyQe1+6aYTTs5yveSkWZPG\naNPeU0bfvbKHANDnipN+qF5RIxbtMltsYnxPKrdlDD1epJxOm5dNpxQAWk3u017ftflL1tvhXdy/\nQlWq1dLlgKSjKJWXAKfg6NEDcvFJPmpTa1rfdir3WSP5RcQqPiZMfTo2VZ/xS42+fevmquNvf+jw\nqfPydL+s8QN7qN/oeUafVVToNe3cvl3Xgm3jd7Klv2ufkXK+4iGn00c0duRInbycdpeT3w4BIAAA\nAIDUIgAE0om0CgCt4VOC2JjEsCU+Ls4IYq6LizVWX005AEwUHXWLix/vNgC8V9YVZ6MTX1NUstcX\nb3m9icFilOVx0j854JqPQsPvJXhMnYc9AIyNTXIBq2WcxF9fpTfe8rclv4dkTGzcLQPABDFJjldy\ndx8Apom4GMUk3ugydSw/4+vto+i4G+cW/vcIAAEAAACkFgEgkE7ct3sA3sH5PWtU99v6mr9kiZYs\nW6+A0OQBz42iQn20eoVl30Vz1KhOHW0+4WL2IIE93gPwdiJ9ndXs69oaO3WOlixZKmf3pHf+S0FU\nqLY4Lrfsu1iNv6+neevu/bLrRxEBIAAAAIDUIgAE0okHFQAi7T1qASAeDAJAAAAAAKlFAAikEwSA\n9oMAEP8FAkAAAAAAqUUACKQTDyoAnNT/VzXpOCjZvfJuZ97Q7vqhbQ8luUvcPYsOuaoxI4Zr8ZKl\nmjByuHYddTZ7bi/q2iVVKfuulu07b7ZIkcFemjF1slavXqOxQ/powNiZZo/N8R0rVPn915U9e3a9\nX/FzOe48YfakHXsPAGNCvPTDl59r3sZjZssdxASr7fe1NHzBZrMhbRzevlrDx03VksWLNGb8DIXe\n6laDN3Ac00MffdkwxTF88eg2de/WRcOGj9buQ6fN1iSi/FSrUjmNW7zXbLCIi9Zlp2P665fm+uir\nFvJLyw/HbRAAAgAAAEgtAkAgnbjfAaCHu7tCo1KO+eLjkt/3Ly7OtjJCXEyk8b83sfSn1RIILvuX\nKFuO/DrsmfhctcoWUf3uk80qZesXTlffPj1VoMATWrDznNHmcWSF8uZ7SWeSLMLqOLqzMj5ZXH4R\n8dq7bqF2n3Qze6Qwz6PKn9FBHQYuMVvShr0EgBEh/nL3tK2anJIbFwRJEBGR8riJjb39/SX/jV+/\nLafStX82K8nt0DLlfKKgDruFmi03iwn20rBBA9Xs+8/0crnPlXT9j0MrJ+ixDDm07pi72XKzfRsX\nqX+/Xnr1mTwaPDv56shWjkM66fHny8j/P1ofhAAQAAAAQGoRAALpxH0JAGNCVLv8a+o0erlReh1f\nIwcHB/WasEzXAkIUcM1L/ytVSC9WaW70+3hc0ntFsujF9z7Thm37dOzIXhV+wkF1O480+v18PFXr\nvZf1zCv1jPpGPu6uOnny5C03Tx8/c89EHWuVU8HiHyrp5K3xneooU4Gi8k5hJlVcmK96dftdF69F\nKMbrsHLlyqT5ZgDoeWK98ffN2po4I/Dy9oVG29L9F82WRB5HVqv0B9V0NeLfLgV7ew97AHh682y9\nUPQNuZhZ2h+NPlKWQm/pyDkXRURG6uyuJcZ7OmT2IcXHRGj38jFG3azHEB04cFBLJ/Yy6o1nrlpT\nZF05uUN5LHWnobNtvzCJ+JhIOZ0+leJ4MbbTZxQRnfz4WFcdzmX5fX0nOZotFmFuKm4Zqz/0nmg2\nJHdkyyL9PWK68Xjob1/r2dKfXA8A9y4abv4963Rwxyb16/2HZq/aYfZaxIXpn57dtP/8NctjDxXJ\nnVn/pBAALhn0s3IRAAIAAABIhwgAgXTifgSAceFXVbJgDrX9Z5GtIdRFRZ/KrxlbEy9t7PrNR3rl\nkxZmJX3zUVF92rKnWUkNvyij1+snzrT65/tqKlrqe7NKbtX0MWrWrNktt3mrtpp7Jur9QxXlL15W\nUWZtNcDyHE++8J4izDrB8W3zVa3mt1qweKnmz5+vqaP6Knu2x9Sh11BtO3rG3EvasXa55i9xlIu7\nq6q/XVAvV/z+pss9Ny0cr75j55hV2nrYA8Ctc/vLIXshXQq21UsGdlThtz5XSEJi5ndBxRxyatCs\n/bby/F455Mip2QcuGXWUxwnlz5pTEzacNGrJV+8WcVCnITe/39EBHuratmWK48W6tWjbQW5+4ebe\nNrG+51Qwo4N6TrQF21ZxPuf1nIODOo+68T2PVY8f6+unHoO0ZNFCY9w0qF5WeV9+Q/MWLpFveIx6\nNXxPmUpVSTZG+jSvolyvV9PFMwdU/bOamjl/sfGz86eNVMEcj+mHH//S5l22vz8BASAAAACA9IoA\nEEgn7ssMwNgITZ84UjtPXNDVq1cVljRlM3X/rope/bytWUnfVSmmz9v2NiupSc0P9GbDTmYlDW34\nhYq/2dis7l10wGW99kIhLdht3vcvxkdvF3tWs7YmBnq3EutzQrkfz6TlhxMv603gd+mgSr1YUF3G\nJA+EnA5uUM+/h8onOEoxUVEKD/bX6tWr5BV4Y9x49x72AND93AENGzdJvr6+8vb2MVuTCHE1AsDh\nC44aZbDLISMAXHDMdhzifc4ZAeDUbU5GLQXpvSIO6jbKDKLTwIoxv6vYe9XNStow5Q8VK/u5wlIx\nmXN0t3p6rsxnZiV5ndqqvDmf0MazideOD/65nqo372VWScRfVfE8mTVy8RGzIdHKYZ2V6/lyuvVF\nyGmLABAAAABAahEAAunE/boH4IJpw9StV2/169fP3Pqqds3qmrf5sM6fOqipk8Zr/IRJ2nv8rE4e\n3qbJ48drwqRpOnDSUh/ZpikTxmnixCk6cPqinM8e09TJtv237ElhgYR74HLumFatWqk9h5P/3kMb\nZ+nTz2rpgvfNsUpUsLemTZusS16BRh0dHqANa1dq5ZqNuhoQZrQltWXVfI0cNVpjRo/SiBEjjG3k\nyFE66uRh7pE2HvYAMMT3orp375RkzPRTjy6/qHajH+UVEKoNyxdo/MSJmjZroS5ccNaaZfMsY2Si\nZs1dogsXL2vNivmaYKlnzF4oNw8f7d3iqHHjJ2rK9Nm6eMXLfJY0EBepnZvXa/W6DXL3DTEbbYb9\n3kL12vdKcaGPE/s2ac6yNTfdx/Ls4b1atmKVduzar+CIW9yvMDpY86dP1vELye+NeO7QPs2eOVUT\nLZ+XRUtX6Wpg8lmL9wMBIAAAAIDUIgAE0on7EQBO7tlcufM9ry69+huXL86bN1cD+/ZQr8HjFBCe\ndgsyILmHOQCMC3HTBy/l1duVamvMpOnGuJk1bZI6/faLVmy9v8Ej/h0CQAAAAACpRQAIpBP3awYg\n/nsP+wxAPBwIAAEAAACkFgEgkE4QANoPAkD8FwgAAQAAAKQWASCQTthNABgXoz3rF6vmJ1U0cMJK\ns/HBiIsK1sa1q7Vy5UpzW6W1a9fo9AVXcw8pJjJcVz3cderMacWk0eqtBID/XniwryYM+kMVylXQ\nCdcAs/UBiovQ1nWrro+dfcfOmR02EUFXtSphXDk6ysnV2+yxiI/Unu0btX7zDgVH3r8lgQkAAQAA\nAKQWASCQTtjXDMAoVSqWR63/mm3W/72YiECN7P+3Nu49Ljc3N9t24ZA+++RTnfaONPeyhTNje9RS\nwVdKK9qo7h0B4N25dGC2cubKqEOXg8yWByM21EsD+vfTFd9AeV/1sowdDyW9Y6bX+YPqP2SiAoMC\n5eXpIY+rttWD42PC1b9zW/UdM0eurle0f8tKvVIwq54s8YmC0mpwJUEACAAAACC1CACBdCKtAkD3\nswfUrGFjDR42RN07NFXlqm0VGy+F+V5UveqV9XPPfvqjfWMVfOEVbT1zVYoJ0/yxffTE49n025Cp\nmjL8T33wVnFly/OSVm/fr0kj+qjq/8rqsUy5NGH1Acv+oZo6qLNy58ymdgOGqkf7pnqjaCHlerq4\nNh13MV9FiCoWeUJteicGgKtnjlL733pocP+/VPzZQmr752ij3fvCEbVs1ED/DLP8rp9bqPKnjRUR\nZ3QlivBT75/bqmnTpilvzVrJyd3f3PnWerdvoGHzt5tVonG96ui5195/hAPAOM0dPUDN23XW8FHD\nVK3sBxoyZYfRs2X+SFWsXE2DhwxRpXdK6JPvOynKMp6uOp9Qg8/eVc6X35Pj2pVq+X1t5c+VRZ82\n7KK9e3epTaN6Kv58fhUp87l8wmPkcW6/vir/up58s5zGThqr2tUq6/HMGfTpD79eP95nd01TjpyJ\nAWBkkLu6d/xJg4aNUIdGX+mF4m9q52k3o2/h+IFq1vpXDRs5Ql9U+ED/TFlvtCd1ac/GlMeLufUc\nMv6mlYClWA3t0U6vFSssBwcHPVX4La3Zd9bss/SG+apt3c/1YqGnjP63K32tc143r1CdYO3Ybnrt\nw690PyYCEgACAAAASC0CQCCdSKsAcNOcv5Ule279NXKGAhImuln4e1zS7n1HFejroWP7t+mN53Kr\nZY9pts5wDxV/0vIzc2yhj9V3VYqpWuteZiUNb1JTL5f8zlb4XVKuPDk1fMNRW22xZVZvOWTIp9N+\n1jQnIlkAOGtAK733WV0tX7pEixYt1qyxw/TJx1W16qi7jq0dryxZc6nnsCnyD7elJPHxN6Ql0WFa\nt3Sh5syZk/I2d768A8PNnVO2f/lYfd+2h1kl98gHgHEh+urDl/Ry6c+0cd9Js9HmyN5dunjFS5cu\nXNDMIb8qZ7YCOuEZZfRtGP+nsj39lmzz3yTvs5uUNWcWbXMyWwKdVdQhpwbO3G+U0/t3VIYS5ZT0\nSFV+tYDK1/vTeOy02xYAHrkSYRkEwfrovaL6Y/hUy/u4SIuXLFW3Fg1U7YcOio6PUYOPX1Xht6pq\n/e5jxs+m5JrzuZTHi7mt3rIrhQDwBmFeeq1QdjXoNcVsSM7j2Dplc3DQ1A1nzJZEO5aOV4c/h5pV\n2iMABAAAAJBaBIBAOpHWlwAH+zjr98ZfyCHrEzrlHaZtcweoZIXaCjZnW338Sl416z7dVlgDwLxP\nqMf0Tbba4uuPiurTlj3NSvqnwecqWuoH43H8tQvK9WQuDV2XGESdXTNNuZ98WZfDrFWYEQC27TPH\n6JvyZ33lKvSWQozKFB8ld08fs5BCfV31R8vacsj0uLad8TJb00a0/wXVqlFHV0JunFpoYw0An3/t\nfcWY9b162ALApHnrwS1L9UqB7KpY7zfFRkfoqyrvqtuE1UbfxR3TlDNbfp26arsgdv24P5X96Tdk\n5oFyP75WWXNm04aTnrYGXye97JBDg+fYXu+0v9srQ8nyCjYqmxYV39AXbQcaj8+ZAeBRN8svjA/U\nq/kzqnnvWUZfAn/fqwoOTUy2j2x3VKlnc+nd2m2TXaablqb266yBU1aZ1c06t6yvNUcS7ysZ4n1J\nI0eNv/5ZswoLDb1z2PgvEQACAAAASC0CQCCdSKsAcNawlnqz/GeaNnOuxgzqpq8b/Kig6Hitn9Lb\nuGSxao16mjptsiq+WVDPFHtHGw6e0dblU/V4juyq1byrXK546vSezXr35bwq8kENHT55SRfPn9Q3\nH76mpwq9oS0HnBQX7KaCeXOo9Cffas6seRo35C9V/rimzngES/HROrh+kV58IofK1GikYxdtwcjU\nQT8bz+/gkFEFni+sToOmG4HIvJG/6/UPPtMUy+sd909P1anXUh6BSaYu3rM4/dGmoeZsPGHWyZ09\neUgtvyyr3E8X1rxVmxQUZqZZ9+ChCwCjPfTJhy+pVad+mjNnrpp+96WGzN6o+PhI1SxdWJmffEFt\nOvfV6H+6Gsewbvs+cnZz0a9NPlO2xwtqmuMuebu7a3L/X5XdMo5+6TdZV654ad380XoqW3Z90/oP\nXfUL1fzRPZQpZx517j1Uc+fMUcu61dX+z7HGawjz99KwHq2UPXs2/TF8ujGjM9T3oqqVKW48Z/ac\nufXauxW187Q1XAzTJxXfVrNf+xgz+ZrXq63+E5cbv+de+V7co3deeUEfVK6pAUNHavbCZfJKMpV2\n98qpeuHZZ1SrbiMj5Fu2csv1y3vD/VxV53+lLK83gzJmsI51c8taQAcup/3CJgSAAAAAAFKLABBI\nJx6qRUCCXZUrTw6N3JL8clHYsAhIymb/86scXimvlOdh4t8iAAQAAACQWgSAQDrxsASAYde8tHzh\nPE2YMFHTZ87VOee0vVzXHhAA3uz8kf2aOXWyJo4fryUrNyvUupII7gkBIAAAAIDUIgAE0omHagYg\nbosAEP8FAkAAAAAAqUUACKQT9zsAjIuJkre7i46fuZDmixHcjfi4WIUEB8jNzU1uXt6KTccTwqyL\nYXhYX6e7h4LDwpMtmpGShzkAjAgP1SWnM3JxS1yg5UGKjYlWgJ+vMU58rgWarfYoTj5XPY2/0z8w\nRDFxd75QmgAQAAAAQGoRAALpxP0OAK2Z1W+1y6tYlaa2hgdsxsC2Kvp2A7Oycb9wRJ06ttfAAf31\n3ecfyCFjLi3cftrstfwNsdFyd3ZSh6/LyuGpIvK/y9Aw0v+ySubPrBffbGK22Azr2ly/DZqi0LBQ\nLRzdS01+G2D2mGJ89HbeJzVz11mzIWUPbwBoeUMj3FXiiYz6Y8xas+3BavHBq2rSa4JZWcTHat2i\nKWrfsbP69+2pIvmz6Pm3PpNPWPI1gOeN/EOvvfmuSpcubdnKatHWxEVgVk7ur3otuyowOFRHN85S\n7XrN5R9tdt7BorG99NLLxfTee6WVP1dmOTjk1rRVB8xeydtpt2p88aWOXfZRoPdFffflF9p8/IrR\nF+LlpM/LldJbpcvozVeLGQuEvF+juQKikod9bWqW0hfdR5nVrREAAgAAAEgtAkAgnbhzABin4/u3\naulyR61dt17nnN2MVu8r57V6paO277UFZT4up9SvVze1bd1So6YtUUSSbKHbt5VU8pMWxuMTe3Zo\nleNK7T9z0aidTh3USsvv2bU/MXBTbLiWzp6ojj+2VZe/hsgn+OYVcmPC/bVkwWzNmjUr5W3OCvmH\nJg9nrOYM7aiS7zU0q5Q1+fYLLdrpZFaJ5g//WY+/9KYC7iIAXDdnjOau26kRTb7Uq6Wbma3SoDY1\n9PQrHyrpK632/nOq0jzJMQly1XvPFNTcPTe/pqQeZAAYHR6o9atWaNXqNVq/YbO8gyOM9lNH9spx\nxXJddPc26iM71qhLp45q1a6DVm49aLQZojxV6uls+nP8JsVE+Gnz+rVatWaN3LxDpfhI7d68XitX\nrdXFK4n3fgz2cdX4EYPUpmULjZyxPMVFPvxcnFIeH+bmuGF7ij/X7qN31NRcKdjqxkPueXKTqtRu\noICEpXgte4z8vZ4cMubQJ3UaaNnmJH+bxeYZfSx9BXUxSXr8d+sqKli6hmLM+laC/a/J/4YVqif2\nbqYn36hi/Gykz2nlecxBw+bvsXVanN0ySRkyZdfBK0Hy8bauYJxEtLtefDyLRq84ZjbYtPnyHdXp\nNc6sbo0AEAAAAEBqEQAC6URqZwCun9hNWfOWkLeZQ/i5HteEGYuMx5e2z9NLxd+Tn1FJdco9r/81\n+t2sbAHgKx8nhl7Nv/xAJb/rYFbSyCY19XLJ74zHJ9ZNVblP68rLL0DXrvlp++whxoyl0WuPGv33\n6lYBYEx4gGaOHaDnn8oqh0xPaPi8LWZPorsKACMD1LfXn7pgvnF9vqqs1963haGWTlV+I7ve+aar\nWdv8WuMDPfNKucRg6CEIABM0+qiEytftZFbSzpXztWbPOePxxN5N9OGXLY3HCnHWsw6Z1Hf6bltt\nBoA9xqyx1REeKuaQUwNn7rXVCleZog76eeAsoxrze1M1+nmwAvz95OcfoJ5NKhrjxMnHFjzeqxsD\nwARXLp5Up1Z1jed66a2qOuuewuXB8ZGa0Lutsc+cbbZgu0f9d5X5tf8pxKhsJg/saNmnkC5eu/Nl\nt0n5Xdqnz2t8I48QW2x8aLntM+J4+qpRW3me2WG0jU4SCibo/3ND9Z28yqwSEQACAAAASGsEgEA6\n8W8uAf6n3RfKXayyQkODNWrkOIWauUV8VKgG9+isUbNXKSQ8Ut9/8qqqNutu67S4MQBsWL2MXqvX\n0aykIQ2/UJHX6hmPR3WsrndrtjEe305cdLhOnzymY8dusR0/q/AUVnxNzQxAj0OLlS3n0zrskfz6\nzH8dAEb56LNXX1KxEiVVtGhRY8ubM7uyZM2toq9WlFtwvPo3/0QvvVXL/AGreH1SuoAqNe5t1hYP\nUQCo2GC9WyibGvaaqWvOhzV17mqzw/JnuJ/Rr+3aat3eU4oKdtUrGXJpwAwz4LseAJqXAPtfVNEM\nOTVo1j5bLX+9V8xBvw6ea1QV3iiu30fdHGLdKDzAN+XxYW5nL16+aXaf1a0CwKR6Nf9Ur39kBpop\nmD/oN9Vu1894vGJ0Z2XMUVyeSfLJPs0qKv9bnymVVwFbxGnOuKGas8YMTU3BrvuV0yGDpqw5ZbZI\np9aPk0OGTNpzOchssYxrpwP6868B8g5JOXAkAAQAAACQ1ggAgXTi394DsM3nr+mxgqUVkOS6xbG/\n1JFDlkI65nxFZ4/sUPFnHlfpOq3ldMnd6G/98evK8+oX1y9z7dv4U+V6sayueF3V6ZP7Vbt0URUo\n/In8g0MU6XtRJfJnVvYCJTR6yhwtnj9dHTp20jm3hPmF9+bmADBOEwZ20x9/j9J5Fw9dOHVYv7dv\nK8ddiWFKgul/N5NDjmfkHpEYGcXFhmvHlk0665K6xSt6fFZGL7z6g1lZRalD/er6c9wKxcfHa8XE\n3qrTtHPyUOhhCgAtYgKdlT+Dg2q2+8dssYpT5dfy6eXy38nd3U1bFo1RDgcH/fz3VLlbF9kIc1MB\nS92yj22Gn2Ku6Y38GfSl5Xdc9XTXtpVzlT+7gxr8PkxhEVE6s3WeMcPt9Yq1NW/Jck0ZPUjd+w5T\ncPjNl33fjRsDwDCfC2rXurXmOm6Rh4eHtqyep7btu+hqsO1IXXM+oKZNm2n55gPysPx9G1ct0tSF\nq5NdXjy+Vzv98FM/xcbF6+yOBfr4i6+vz+JzP39Cm3fsSTGMjIuJ1JT+HfXU08/pq7r19NVXdVSn\nTh29X+Z/2nzM1djn3O4l+l+lmnILjFJE4BV983klLdp5xujzOH9QZV8vrDc/qKTv6n5r/GyNapXV\n+Lf+ikyyCg4BIAAAAIC0RgAIpBN3swhISiFFTHiQPDzNECwuSv6BwcbD2NjEpDA2JvFxdGig3M39\nI0KCU1iNN8ZYmTTy310deUepmQGYkqSvPd7yN8VdX5I3XqEhfvr7947acybxEsy7YQ3wUvSQBYCG\nlAaJReA1L10LDDMeR4YGKSzCFoDFxiYe6Jgkj69ddZd/iO3y6aDgxNlsCaLDguTmkXhfwLSSmhmA\ndyvpWEoQHRmhvatmq9vAqWbL3Yq71Vt/RwSAAAAAANIaASCQTtxNAPgw27lymr5r8JMWLVqkJes2\nK+weA8awoGs6c/aCWaWtiGAvrViySIvmTleDOl9rl9MNizncIF0FgA+5SX/8ovbd+hjjZPvexJV8\n74v4GF06f04+QWlz/8J/J1rbN60y/s6fW/2ggQvXm+23RgAIAAAAILUIAIF04lELAO0ZASD+CwSA\nAAAAAFKLABBIJwgA7QcBIP4LBIAAAAAAUosAEEgnKlSooMKFC+vLL7+87fbDDz+oadOm+uqrr1Ls\nT+9b7dq11aRJE3399dcp9tvDVqtWLWNhjKVLl5pHN234+fkpU6ZM+vDDD1N83kdhs4576/ixLqCR\nUv/DtFk/A9bP8vfff59i/522J554Qm3a3HmlbgAAAAAgAAQeIqtXr1a+fPn0zz9JV3V9+Dz//PM6\nduyYWQGpd+nSJRUqVEixsWmzyvCD1q9fPxUoUECOjo5mCwAAAACkPQJA4CHQsmVLZcuWTZs2bTJb\nHl6RkZHGTMe1a9eaLUDq7dq1S88++6wCAwPNFvtw8OBBIwisXr26QkNDzVYAAAAASBsEgEA65e3t\nrTfffFOlSpWSl5eX2frws87cKlasmJYsWWK2AKm3YcMGvfjiiwoLCzNb7Iv182G9JNh6ea89BP4A\nAAAA0gcCQCCd2bZtm3LmzKlmzZqZLfbntdde06xZs8wKSL3ly5eraNGiio6ONlvsl/UektmzZ1e7\ndu3MFgAAAAC4OwSAQDoxYMAAY+GIKVOmmC326+2339aECRPMCki9efPmqWTJkoqPjzdb7J919Wfr\nwi/WS+fPnj1rtgIAAABA6hEAAg+Q9X54NWrUMBb2OHr0qNlq/8qUKaOhQ4eaFZB6kydPNi6Lf1RZ\nPzcZM2bUkCFDzBYAAAAAuDMCQOABOHfunJ577jlVqlRJISEhZuujo0KFCurbt69ZAak3atQovfPO\nO2b16LLOBHzppZdUvnx5Y4YgAAAAANwOASDwH5o/f74xe6dnz55my6Ppk08+UdeuXc0KSL3+/fsb\noRcSdejQQVmzZtXixYvNFgAAAABIjgAQ+A8knKCvWbPGbHm0ffnll2rfvr1ZAan3xx9/qGrVqmaF\npLZu3arcuXOrfv36iomJMVsBAAAAgAAQuG/8/f31/vvv65VXXpG7u7vZCqt69eqpefPmZgWk3s8/\n/6zq1aubFVISFhamL774QgULFtSBAwfMVgAAAACPMgJAII3t2bPHmIXzww8/mC24UbNmzfT999+b\nFZB6rVq1Ut26dc0KdzJ16lRlyZLlkb/tAAAAAPCoIwAE0siIESPk4OCgMWPGmC24Fevlv1999ZVZ\nAanXoEEDNWnSxKyQWm5ubnrjjTf05ptvysPDw2wFAAAA8KggAATuQWxsrDEbKU+ePFxq9y906dJF\nn332mVkBqWcNjn/88Uezwt3o3r27MmfOrBkzZpgtAAAAAOwdASBwF1xcXFS0aFGVK1dOAQEBZitS\nq1+/fvroo48UFxdnbNYFC6KiosxeIJF1XFjHR8JYsQbHrCCdNg4ePKhChQoZ9wsMDQ01WwEAAADY\nIwJAIAUhISEqUaKEJk+ebLbYODo6KlOmTPrtt9/MFqTGuXPnjMujb7fd+F4DVvPnz09xvCTdjhw5\nYu6Nu2ENVhs2bGjMZN68ebPZahMREaFPPvlE27dvN1sAAAAAPIwIAIEbWFfstZ4IJ4QLzz//vH76\n6Sc9/vjjWrp0qbkX/q0lS5YkC22SbjVq1DD3Am5mXTU6pXFj3aZPn27uhbRg/Y7LkSOHsWjIwIED\nk73X1gVFAAAAADycCACBJA4fPqzHHnss2UlvwlalShWFhYWZe+JuWO/dduP7mitXLhYlwG35+fnp\nqaeeumnsNGrUyNwDacnJyUnPPPPMTe+3deP+iwAAAMDDiQAQMK1YsSLFE94bt2nTppk/gX8rPj5e\nb7/9drL3k0t/kRoLFixINm6sl+hb7w2ItGP9B46yZcsme59T2qz3PrV+lgEAAAA8PAgAAYuRI0fe\ndJKbIUMGY9bfzJkzFRQUZO6Je+Xq6qrs2bMb7/Gnn35qtgJ3VqdOHWPcWO/Dab2vJO4fX19f4/Lq\natWqKVu2bMm+G61bgQIFjH0AAAAAPBwIANORZcuW6f333zdmSLH9N9t7772n/PnzJzuxLVy4sBH8\nVaxYUaVLl07x5+60vfPOO8bPjhs3zjy6aad79+569913jedI6bnT+2Z9X6zhgfW9Ll68uPG3pLTf\nw7BZj4H19Xfp0sU8OvfHyZMnVb58+RRfw6OyWd/nkiVLGuPGejmw9bOb0n6Pwmadpbdr1y5zdKQN\n6+y/b7755qbvFev7/uGHHxrfiW+99ZYyZ86c7Pvytddee6g/w+l5s/7/gblz55pHCAAAALg3BIDp\nSJ8+fYyTLNiHpk2bqnHjxmaVdipUqKChQ4ea1cMrMDDQfPRwGzZsmHFJ5P20fv16FSlSxKwebczG\nlV5//XVjZnJaCggIMBY8Onr0qNmCB+3LL79U586dzQoAAAC4NwSA6Ujfvn2NWWewD99//72aNGli\nVmnHOkb+/vtvs8KD1q9fP2N23v20YcMGvfTSSwoNDTVb8CizzoScNWuWWaWNhABwx44dZgsetM8/\n/1y///67WQEAAAD3hgAwHSEAtC8EgI8GAkD81wgAHw0EgAAAAEhLBIDpyH8VAF67fFQtWrfWGbdg\ns+X2gt3PqHWrFjrifM1sSTuhAb7y8Pr3vzcyMtJ8lCg82F+urm5mdTN/by+5eXqb1a3FREcpLg0W\nuEzvAaDrie1q0aa9rvhHmS23F+p1Vq1atdb+i3d+D/+twGtX5eUbYFZ3EqfQ0HDz8c2C/X3l6uZh\nVsmFhYYaIVriFqLwiGiz1yYqIux6f1DwnQO3RykA9L14QC1btdHZqyFmy+0Fuh1Xm5aW7w7XtL/c\nOyTAR57ed/PdcbvxHi9P9ysKDAkz6+Tio8N1xc1N4dGxZktykaEBcnPzVEyc2XCfPNwBYKzG9P5N\nQ6cuN+s7mzvqL3Ufdj9WX4+T2xU3/au1pOPibvnfB+vY8fDyMat7RwAIAACAtEQAmI48SjMAD62e\npHcrVFeAeeb14zdV1W30EltxG2d3LFNWBwdV/OEXs0XatWycPvu6pcLMk7Jlozvr5beqyCfC1jC2\nR2u17D7SeGz1W/3Kqtrgd8upfnJRAe6q8loBOeR9Se63zpdSjRmAd7Z+Rj+V+6yBbJFMlOp/Uk7D\nFmw3qhsdWDND5StU0j8jRmpQ7656KrODnir2oTwCbQHeysl9VadxZ8spvc34Ho1V6qNvjXERHxOq\nzm0a6ve/Bmrs2LG2bUgvvfLK2zrjkxAmR2vsoF4aMGKcsXjL2LHjdMHT3+y7NWYA/rf2OY7Vex99\nqWAzg2tdp5J6jne0FbdxfNt8ZbF8d3xUp7fZkiBS3VrU0f9qNdFpl5vDm4gAF9X+qLSa/tpfPsER\nZmsiz9O7VPbtkuoxapbCom+RDKUxZgDeo2h/fVXpfY1dcdAoT6ybqjKWMXXtVtlwXKyCg4L0T/Pq\nypKjjJLF3/FRalXrM42Yk7goy5CO36rRL4PM6u4RAAIAACAtEQCmI2kXAEZp5rhhmjJ3keZOG6cu\nPf/W5i3bdPCYk0KCr2ncwN9V9v0K2u8arHB/Nw3o3FofflpTe48c0a9NvtTzLxTWPzPXGL8pIuSa\nJg7toQ9Kl9eWI15GW1L+Lqf0648/6sdbbF3/6q+QG6ZXhF05pFyPOWjgst1mi+R1aJ0yO2TUor3O\nZssN4sI1sGcXnb3iq1/rf6AyX/9odgTqrSdyqE3/hWZtFavqJZ/Up60GKC7glHJkdNCiXRfNPkuv\n/xk9md1B3aesN1ukdXNGa9aqfTqwarSyPl1YbilPAPpXHngAGB2qCcMGas6iJZo+Ybj+6POPtm7b\nrqOnnRXs76nBf7TVex9W0/mAaAV5OKl7m+9Vtc73OnR0v1p887GeL1xEU1bajlFEqL8mD+uj8u+/\nrS1HLxltSbmf3q+fUjj+CVvvQSOVfJ6d5ZgfXW2sIjpj5zmzRTq7bqalLau2n/czW0zxcYq5IVsJ\nd9ur/Pme1n6XUCnGU89nzKi/picNDwP0/tOP6bvOieFvUr1/+j5J2BivAR3q2lY2zZhddVv+rqtB\nqZsZaVcBYHykpo8fomnzl2jO5NHq2sPy3bF1mw6fPKuQQF+N6ttZZUu/p1NuwQrxu6I+vzdX+Yrf\na9/+w/qpQS09//zLGjZnnfGrIoJ9NWFQD5V7733tPHvzd4ev6yF1TGGsJGyd+o5QyA0z6QJd9hn/\nADB68SGzRbpyZIVx3JYeuMXs39hQy3dHN531uKYuH7+v8l90Nzsk5/3LlcHys7O2XTBbkls24lfL\neHhKlwJSntL3V5NKyvlied08H/n+Su8BoHV2cd+/B2nZimX6q2sXTZ67VDt27pSXj58Ob3dUjU8r\nq0OP2ZY947Rl2XR9XrWies1appVzxuqNkkVUoWZTeQXb/sNxau8mfVPjUzXt0MOobzRvaP8Ux0/C\ntvmwk7lnop+/elf5ytRJ9o9ADT54QaXrJPx3JWULerRUvqcq6sa58/1afy6H7Pm17uAlBXmfV+3q\nNbX5qKvZe/cIAAEAAJCWCADTkbQKAFvWfEtv1GxhVtKc7s2Vq2AFJVy05nFwtbJkflJbzJDl0Oqx\nypQ7vy6Yi2ue2zZXDjnyaK+HLQULOL1FuR1ya+WBm0/i42KidM3XV7632Pz8A266XOrYhlnGCfvK\nfS5mi+XE/uIuZbO0dZ+7yWxJdPX8AQ0dO8OspB+/Lq0Pv//VrKTz+1frpUKFNHiqo3FJ4OkDa5Xv\nMQe17Gs7QV49sbcKFX5FK7ce0jU/P62Z3t94/rk7LSf9ceEaOXiQvMwZf3uWDlHWZ4rcdIJ3Nx50\nAFi9zHP6uNVfZiUNaVJdz7z+tVlJJ1ZNVqYcz+mEjy2aWzO1h7IWLCo/83jtXDhUDk+9oItmGBp4\neYvyZHfQqn03B4AxUZEpHv+EzT8g8KYZlxtmD7Ych+zaez7x0l+3A8uMYzNm0zGzJWWxIZ764dtv\ntf+ir9liGcfrZ+rZgs9r8pLNuurrr0Mb5yq75Xf1nJQY9CbYt2yc6rdJOVCwunxkk56w/Gyn0cvM\nlluzpwCwbcWSKl3lN7OSJvVqoCdLlDUryfXQPOXMkUH7LtpmRh6ZP8zyHhfWJfMDc27TJGXLmFfH\nPWzhqdfp9cqSM6s2nrr5uyM2JvK23x3X/AOvz+ZMcHLtaGN8LD6eGPZ5n99ttA2ZcXNw5XH2gIaM\nswZNNj//7219VLuPWYWpXLEn9VrFxroaEKSY6EgtGdVNmXPm117na4r3PWOMgSZdhyk4LFKRoYHq\n2qCKni75P/lZPjKX99i+x/6esU7hUTEK9nPXl2Vf0vs1WinqxsGextJzAOhzbJ0yOGTTurNmiB95\nWS8+bjk+cxP/wafuq0X0RaMhZhWl117OrwaDEv+eyiWe0dddx5mV1OGbV/RBzaZmlVxIYECK4ydh\nC4+88Z8eolT1xSf1Ud12Zm3T/psSevzN6re9HPhWAaDVqnljVe/LT40x0eqPkdf/e3svCAABAACQ\nlggA05E0mwEYE6ymtSrp1/7jtH7Ncv3aqZvOXU08ZXHdu1xZkwSAe5aNUOa8z+i8v+2U5dh6y4lt\n7qe139OW/PgeW688twgA70ZcmKeK5Mum7tMTw76jayfLIetTOpmQxJniQz3VpF51ValcSRUqVDC2\ngk/l0uP5CqlC7e91KejG07U4VSn5lD5u1susk4u7dkZ5Mzvor2kbjXp0/9/0cdWq13/36yUKK2Pm\nbHq/fAXN2nnC2OduPegAMC70qr6qWk69x8zU2pXz9WuXXnIPTJyrdHT5+GQB4PLxXZTj+Vfka54v\nb5z1jxwKFNUlMwD0vbBBeXKmHADejQjv08qb4zGNXpsY9m2c3kcZ87wstxunfiWxeu5EzVqx1axs\n4m8KXIJUqmBW/dAtMURIEOXnpFrVv5Rb6O1Tmvlj/1TTnmPN6tbsagZgVJAafPE/df5ngtZZvjt+\n+62bLnonXvB4cf8s5cyZQfvNAHDfrEHKmaGYzFLH145RzmxP66SnLQB0O7ZGWXNmSzEAvBuxIW4q\nnCuL+s3cZrZYxrHjKGXK+qRO+yafhxcX4KaWNatZvjs+uv75fiZ3TuXO+7w+rtFMgREx+rrsS6rU\nOPklwa3KlVLFH/pZnixAz2dzUJ8ZW8weKx+9/kwG9Zu1VxEe+5TBIaOWHHE3+6Qwl0PKlSm75u+/\n99lft5PeZwDuWT5WZT+qLse1GzV60F8aPW2F2WNTt+TLqt54qFmFqORL+dV46ByzjlKV4s+q3h8T\nzdp6i4gStwwA78aYLnX15KsfJwnpolX+xafUqPsks05ZSgFgjO8pPf9cQa07l/gPGeN7NFSOAqUV\ndI8pIAEgAAAA0hIBYDqSVgHg6B7NlPfZ4vrmm2/0zddf68tatdSkza867+GvuOhILRjVzZilMGrJ\nbgUFBWhst++NetHOM0Y9Z8hvRj1h9QGFhYVp3dQ+Rt1/6kZFRN06mPk3ooPc1KJBPTnuOKzTh3ao\nRdPWOuVum4IYF+mv39q00OQlyUOeBPUrv6yS1RKDNT/fq9q/a7t6d/pJLX7pIb9kV27G6aq7q7av\nX6YfW/ygnkOnm+0pWz25uxyy5JGrHVwC3KNVTT1T5I3r46BWrZpq3fEPuV8LU3RkmMZYxon1uM7f\nZj3ufvqzWWVLnVlbTroa9YguDY3+pXvPKyIiXBtnDTDqvpNWWMbB7ebJpF6I52k1rP+9thw6pSO7\nVqtp8/ZyDbAFOZHB7mrbvJkWb7YGhPGaM7Sr8fyZs+VU1ixZlDlzZqPuPNJ270hfb0/t3rZR3dq3\nUIceA5XibRzjo9WmbnXN2HjSbLC5dn633iz+sn5o11WLl63Q7GmTte1g4qXJt2NPAeDY3+vp6ReK\nJfvuaNauky5dDbJ8d4Rr3rBfjPd80oo9Cg4M1Mhf61vqjFq++5yCAgM0a9CPRv+MtYct3x0hcpz0\nh1H/M3ezIm+xcMa/FRXoqpbf19eaXUd1+uAWtWjeXOfMRUniwvzUqVULTXJMnG2WVKPXX1Cp//1s\nVlaR+r3ld+rQe4ycXV21ev5Yde034vrl6pH+l1X38481dsE6uVx21pgBPTRybuKMUo8zO1StajU5\nbj0k54tO6t25g1Zsu/3s1bSQngPACM9TKpnvCVX6vLYxjr6qU1s1a32lMXNWGf2+bqdVJIuDin/Y\nWNf8guThtNu4rPvDRt3kZ6k9XY7rhcwOKlWtlfwDQxXo66byL2XWkyUqyt37zvfkTK2FY/vqpx7D\n5XT+vIb3+E1Dp6w0e6R9K2eqRcuO8g5L/J7z9/FSj/pVLOM5j3acuKjwqMTx7Dh5kOo2aa9jZ87r\n8iUnDf+7l5ZtO2r23j0CQAAAAKQlAsB0JC0CwHDv82rdooNuvnuZn/4eNFihaZPfIRUeZAB47dJB\ntWzTxaySCLqkv4eOSpPL02BjLwFgiNcZtW7T+qbLbhXroz79BisNMnGkkfQcAM4f1Utj5ifO0Eyw\nYe4kLdp876HYo4QAEAAAAGmJADAdSasZgG5Oh/XHL+1UqUIFVfmkunr0HaazV66ZvfivPOgZgBeO\n79JvP7ZQRcs4+PTz2uo7eJxcfO/zJaSPIHuaAXjl7CF1/aWt/lexgip/WkM9+g+Xk/sNC7LggUvX\nlwDHRWr5nPGq91V147LrH5q31XzHTTcHy7gjAkAAAACkJQLAdCTN7gGIdOFBB4D4b9jVPQDxUEjv\n9wBE2iAABAAAQFoiAExH7DEAjIuNuWn110cFAWAS8XGKvXE5aDtBAJg24uNib1oxHCkjAEwUHZM2\n9yNNjwgAAQAAkJYIANMRewoAgwN81fqz0nryjRpmywMWE6w+nVqpRfvOWrl2gzyu+pod0sKJg9Tt\nn0k6duyoenf6WUu33tvqvwkIAKWI0ECN/aOxHHI/p8sprsrx3wkNDNCOVfNUOG9Wteo5ymy1iY4I\n1ZF921XlzcIqVb7NvwqtCQDvTWiwv3o0ryaHF95R4jqqD86wX78zFi4xtixPau3RK2ZPIq9zO/Rk\n1owaMD1xoaIzu1epVJHnlNH82VfKVNNx5/tz+fQjHwDGxeqq6zE9mcNBTYbPNxsfkPgYXTl7Up0a\nVVP2vIV0JeiGi51jQ9WzcwfNcNymIwe2q0O7X3T+auruqEkACAAAgLREAJiOpGkAGB2k5QvnauWG\nHXL18FBYVMK6ltLBnRs0ffp0HT7jYrZYdo8M05XL53XugqXNckKzbf1Kbdp9xOyVDmzfoFXrtl8P\nRqLCQ3X54jldcvFRTGSAli1aoP3HL5i9NkPafqVnS9dOFqaE+rlryfxZWrbuhpPMmFA5Lpynleu2\nytnDUyEprDIb6O8rNze3W26BwSknTF1/+J+eeqVqiveg+rNxJT33YT2zsojy0stP5NRfM26+if2/\nlV4CwB3rV2je4pW6cNlFAaGJJ56uZw8Z42DjzkNmi+XQx0bL96qbjh47btSnD27X8jWbri8q43zm\nsBYvWSn/cNsyInExUbrq4aITJ85bqlhtXrtC67bsNfoSrJvWXw4FiyUPAOPCtXH1Es1asNzyu5If\nmV0bVmnewhU67+yia0leb4KwEP8Uj3/C5uNvW006ZRGq9GY2Nes+2qyT+6tOJb32fguzSp2HPwCM\n1UbHxVq8Yr2cXa8oKDzCbJdOWY6/dYzsOXLWbLF+V4TL3eWiTp5xtVRx2rlxlTbuOGDrtDi8e5OW\nr91yfaGZmKgIubte0umzlu+W2CitXbFEW5J8t1hN69deGYqXTRYAxkcEavWyBZq3dK3Ckq1aE69N\nK5dq0bI1uujqpoBw26rRSYUEXUtxbCRsfoG2VYOTi1P3ptVV/vP6Wrf1oNl2sxmj/9GS5Uv09rNP\naNCsXUbbNXdnHTtlfT8SDfn5a2UuXk43v7p7lx4CQD93J82eOVO7D52Qp4/lvwMJH+O4UK1dukAz\nZi80VhxPEBoSpHOnjumqX4iiwvy0fOkSnbjoafTFRQdrzfKl2p1knIUEB+rMiWPyD4mWj+sZzZu/\nWC7eST7b8cEqUTifmo9IHgBePnNIMyxjdtcRJ7PFJsDzkubMmKFdB47pio9v4utNEBejq54pjxdj\nc/dU5E0/lGjL/O56PF9+uScdWrGBevu5x9V0YOJrvLBjnh7LmleH3O78WSYABAAAQFoiAExH0ioA\nPLxqkkp99LVZyXIyNk1nfSIVfWW3MTtl7SlbEtPx2zIqWPEb47HV9+ULqcSHtXTFN9ioe9SvrExP\nltAhJzejXjqivRxyPStPIx+IV+13nlHJinXlFWALDPq1rSmHjPl11ttWJwSAVnFhV/XN5x9rq5Nt\n5l2E625jlsyv4zbI54ijSlX40mi3Wu44T/sv+5jVvRnT+QfLa3pCc9bt1uHdW9Wp5fdq+ONfCrOe\nx8X4qHCux9TglxG2nQ2xql06r0rUbm3Wd+9BB4Axvuf0aql3dMVMYE/vXa21+05aHkXrjdwZ1ab/\naqN907Q/Lcf1GblaDpt118ndv1PWPAW096ztuG+Z2tdyrDJoxup9Rv/V46uMY7ds/2Wjf9QvdZQt\nz0s6cN7DqHcuGGz0T9twyqivB4DmuOnZuq4GzNhg9FlfS4nHHVT043aKj/bT26Xe1nkzMTlxaJMW\n7jxsK9JMuCq9cesAsNcjFgD6nduhEm+Vvx7w7t+0SLvOWI57mIsetxzDsZsvGu0jf62p3EU/sBwt\nm6YVX9fzr9aUq7e/Ufdt+7kcHn9Ru0/bQrANE7vJIUNBXQqwBSbdviunJ18orXPutsWIloz4zRgj\n647axkxCABhoVJH6sV51TVlrrhgbd1VPWPat1HyQ4gKcLGO6rBJioF3bHbX6hqDnXsVFhxszRYvm\nfUzPvFlNfpG2vyHEy0ndu/bSNes4jrisok9k1j+zbQHgjeLDPPV55f9p1/m0+R670YMOAPu2q6uG\nf04yq3DNnD3V+G5YP+ZnOWQuYWu2ePGpjGo6eK7lUbzl++iMCmVxUPMeIxUaaU10Q/VOgex6t0Zr\neV2zjeuGlYvqjc+aGftHeR5R/gwOatVrgqKtvzzaXx8VzauXyv9ghsshyQJA5/0r9Wn1epbfarNu\n/K/GGDvoFqJpPZvr29/HmD3RGj93tvwT/00sTWyed3MAeG7XEuM1LNh82myxvFvuh5U7o+W/fVNs\n37+3QwAIAACAtEQAmI6k5QzAIxvn6e2iBZUhe2793M88gbJwObFbAwaP0Bmni+rU6HMVqpo4+63p\nFy+oav0OZiXN6txYTxesqoQ5HLuWDFPmpwrpou0sXV+9/5xqt/rHVliFXdFz+TJpwNwtRpk0ANy9\nbJwKvFRRIZZz6ZiYGMXEJp9JcWzLYr1X/FllyJxbP/Yacz1oSGrnxmUaMWLELbedB2xhRVKNqxXT\nix8lD+H+alxVeYrVMh5Xf/VJfdy8m/HYEB+s957Moe9+HW423L30MAPQ9/JR1a78ruUkNIM++/5H\neQfbop6oQDcNGThQuw6fkuPk/spQsKgumGfOswY30NNFXrt+3C9smKmcDgW075KtJdTtsPI8nkkz\nt9gCvjGdv9ILr1ZT0jmb31Z6WR83s72v66YPMAJAN+sY9Dmp/PmKaOcZP9s4uOH+XdeunNA3H5dV\nBsvr/fTbNroafPP8Kadjm1M8/gnbko3JZyAmRwB4oysntqnq+6/IIUNm1W3bU8Fm4HXN+bgGDByi\n42ecNKJbAxUoVfH6bLZfqpTWe1V+MStpxsCf5VDsfdniQOn42jHKme1pnfS0fZL/+KG83vhfY+Ox\nTbQqlsqrhj3GGVVCAGj9/T6nNuvJ/CV1zjs6xTHifnqXPiv7umVMZ9Y3LbopyHy9SR3dszLFsZGw\nrd1tm+F6J80/KauBs/fL88gSFXz2JdWuU1vVq1dX9WqVlTNTBpV8u5y6/J38u+LgxiXqO3TSfV31\n9sHPAIzV9MHd9FTu7Mr7XDHNXp34mdu0fLbGTF8k58uX9Vqxgmo42HydoRdU5GkHDZm9yVZb1C35\nsqo3HmpWUvdmVVWiiu2/STE+x/RsjkyatCrxlgwnNk9XxlyP65i39d0NN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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "image/png": {
       "height": 1000,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is what you should see if you do what is described in the previous cell\n",
    "# the  plot indicates  the age cut-offs at each node, and also the number of samples at each node, and \n",
    "# the gini\n",
    "\n",
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "PATH = \"tree_visualisation.png\"\n",
    "Image(filename = PATH , width=1000, height=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the optimal depth\n",
    "\n",
    "As I mentioned earlier, there are a number of parameters that you could optimise to obtain the best bin split using decision trees. Below I will optimise the tree depth for a demonstration. But remember that you could also optimise the remaining parameters of the decision tree. Visit [sklearn website](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier) to see which other parameters can be optimised.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depth</th>\n",
       "      <th>roc_auc_mean</th>\n",
       "      <th>roc_auc_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.524929</td>\n",
       "      <td>0.011327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.522882</td>\n",
       "      <td>0.014414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.517628</td>\n",
       "      <td>0.013480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.518913</td>\n",
       "      <td>0.007791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   depth  roc_auc_mean  roc_auc_std\n",
       "0      1      0.524929     0.011327\n",
       "1      2      0.522882     0.014414\n",
       "2      3      0.517628     0.013480\n",
       "3      4      0.518913     0.007791"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I will build trees of different depths, and I will calculate the roc-auc determined for the variable and\n",
    "# the target for each tree\n",
    "# I will then choose the depth that generates the best roc-auc\n",
    "\n",
    "score_ls = [] # here I will store the roc auc\n",
    "score_std_ls = [] # here I will store the standard deviation of the roc_auc\n",
    "\n",
    "for tree_depth in [1,2,3,4]:\n",
    "    # call the model\n",
    "    tree_model = DecisionTreeClassifier(max_depth=tree_depth)\n",
    "    \n",
    "    # train the model using 3 fold cross validation\n",
    "    scores = cross_val_score(tree_model, X_train.Age.to_frame(), y_train, cv=3, scoring='roc_auc')\n",
    "    score_ls.append(np.mean(scores))\n",
    "    score_std_ls.append(np.std(scores))\n",
    "    \n",
    "temp = pd.concat([pd.Series([1,2,3,4]), pd.Series(score_ls), pd.Series(score_std_ls)], axis=1)\n",
    "temp.columns = ['depth', 'roc_auc_mean', 'roc_auc_std']\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We obtain the best roc-auc using depths of 1 or 2. I will select depth of 2 to proceed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform the feature using tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tree_model = DecisionTreeClassifier(max_depth=2)\n",
    "tree_model.fit(X_train.Age.to_frame(), X_train.Survived)\n",
    "X_train['Age_tree'] = tree_model.predict_proba(X_train.Age.to_frame())[:,1]\n",
    "X_test['Age_tree'] = tree_model.predict_proba(X_test.Age.to_frame())[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to use those pre-processed Age features in machine learning algorithms. Why don't you go ahead and test them?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age_tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>51.0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>49.0</td>\n",
       "      <td>76.7292</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>1.0</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>54.0</td>\n",
       "      <td>77.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>14.5</td>\n",
       "      <td>14.4583</td>\n",
       "      <td>0</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age     Fare  Survived  Age_tree\n",
       "857  51.0  26.5500         1  0.370849\n",
       "52   49.0  76.7292         1  0.370849\n",
       "386   1.0  46.9000         0  0.516667\n",
       "124  54.0  77.2875         0  0.370849\n",
       "578  14.5  14.4583         0  0.818182"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's inspect the transformed variables in train set\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age_tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>42.0</td>\n",
       "      <td>14.4583</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>18.0</td>\n",
       "      <td>7.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>7.0</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>0</td>\n",
       "      <td>0.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>35.0</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>29.0</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age      Fare  Survived  Age_tree\n",
       "495  42.0   14.4583         0  0.370849\n",
       "648  18.0    7.5500         0  0.370849\n",
       "278   7.0   29.1250         0  0.516667\n",
       "31   35.0  146.5208         1  0.370849\n",
       "255  29.0   15.2458         1  0.370849"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's inspect the transformed variables in test set\n",
    "\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.37084871,  0.51666667,  0.81818182,  0.1       ])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and the unique values of each bin (train)\n",
    "X_train.Age_tree.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.37084871,  0.51666667,  0.81818182,  0.1       ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and the unique values of each bin (test)\n",
    "\n",
    "X_test.Age_tree.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fare\n",
    "\n",
    "### Select the optimal depth\n",
    "\n",
    "Let's repeat the exercise with the variable Fare. Remember that fare was highly skewed and therefore would benefit from engineering to spread the information more evenly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depth</th>\n",
       "      <th>roc_auc_mean</th>\n",
       "      <th>roc_auc_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.609106</td>\n",
       "      <td>0.023418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.662132</td>\n",
       "      <td>0.026253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.647950</td>\n",
       "      <td>0.045010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.650984</td>\n",
       "      <td>0.035127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   depth  roc_auc_mean  roc_auc_std\n",
       "0      1      0.609106     0.023418\n",
       "1      2      0.662132     0.026253\n",
       "2      3      0.647950     0.045010\n",
       "3      4      0.650984     0.035127"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_ls = []\n",
    "score_std_ls = []\n",
    "for tree_depth in [1,2,3,4]:\n",
    "    tree_model = DecisionTreeClassifier(max_depth=tree_depth)\n",
    "    scores = cross_val_score(tree_model, X_train.Fare.to_frame(), y_train, cv=3, scoring='roc_auc')\n",
    "    score_ls.append(np.mean(scores))\n",
    "    score_std_ls.append(np.std(scores))\n",
    "    \n",
    "temp = pd.concat([pd.Series([1,2,3,4]), pd.Series(score_ls), pd.Series(score_std_ls)], axis=1)\n",
    "temp.columns = ['depth', 'roc_auc_mean', 'roc_auc_std']\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the best split roc_auc is obtained with a tree of depth 2, thus I will choose this one to proceed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# train the decision tree and engineer Fare in train and test set\n",
    "tree_model = DecisionTreeClassifier(max_depth=2)\n",
    "tree_model.fit(X_train.Fare.to_frame(), X_train.Survived)\n",
    "X_train['Fare_tree'] = tree_model.predict_proba(X_train.Fare.to_frame())[:,1]\n",
    "X_test['Fare_tree'] = tree_model.predict_proba(X_test.Fare.to_frame())[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.44230769,  0.74626866,  0.25531915,  0.10714286])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['Fare_tree'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.44230769,  0.25531915,  0.74626866,  0.10714286])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test['Fare_tree'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_tree</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.107143</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>7.5208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.255319</th>\n",
       "      <td>7.5500</td>\n",
       "      <td>10.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.442308</th>\n",
       "      <td>11.1333</td>\n",
       "      <td>73.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.746269</th>\n",
       "      <td>75.2500</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fare      Fare\n",
       "Fare_tree                   \n",
       "0.107143    0.0000    7.5208\n",
       "0.255319    7.5500   10.5000\n",
       "0.442308   11.1333   73.5000\n",
       "0.746269   75.2500  512.3292"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see what are the Fare cut-offs within each bin\n",
    "pd.concat( [X_train.groupby(['Fare_tree'])['Fare'].min(),\n",
    "            X_train.groupby(['Fare_tree'])['Fare'].max()], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tree generated 4 bins: 0-7.5, 7.5-10.5, 11-73 and > 73, each with probability of survival .1, .25, .44 and .75 respectively. Indicating that people that paid higher fares, where more likely to survive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# and with this sequence of steps, we can visualise the tree\n",
    "\n",
    "with open(\"tree_model.txt\", \"w\") as f:\n",
    "    f = export_graphviz(tree_model, out_file=f)\n",
    "\n",
    "#http://webgraphviz.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m8XNS9pc0fL8uvAiSkZ5Gk01GCUtNAry+dTaGdlM3Ln3bZTy+Ea9DlesL9dA9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gwLQCKyJSwAyexSWgD2adFEbX8oh99GrBKxwrAAvH1gLtrNkcVamDPylvmfGmsltgl43IBO6DNU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\nIiIiItJZwP8AmRLYJikzIxMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "image/png": {
       "height": 1000,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"tree_fare.png\"\n",
    "Image(filename = PATH , width=1000, height=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discretisation with trees on categorical variables\n",
    "\n",
    "Discretisation using trees can also be used on categorical variables, to capture some insight into how well they predict the target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Cabin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived Cabin\n",
       "0         0     M\n",
       "1         1     C\n",
       "2         1     M\n",
       "3         1     C\n",
       "4         0     M"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's load the categorical variable cabin from the titanic\n",
    "data = pd.read_csv('titanic.csv', usecols=['Cabin', 'Survived'])\n",
    "\n",
    "# let's fill na with a new category missing\n",
    "data.Cabin.fillna('Missing', inplace=True)\n",
    "\n",
    "# and let's capture just the first letter of the cabin, ignoring the number of the cabin\n",
    "data['Cabin'] = data['Cabin'].astype(str).str[0]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin\n",
       "A    0.016835\n",
       "B    0.052750\n",
       "C    0.066218\n",
       "D    0.037037\n",
       "E    0.035915\n",
       "F    0.014590\n",
       "G    0.004489\n",
       "M    0.771044\n",
       "T    0.001122\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Cabin')['Survived'].count() / np.float(len(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that cabins A, F, G and T show a low frequency of passengers, so I will re group them into a 'Rare' Category to avoid over-fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin\n",
       "B       0.052750\n",
       "C       0.066218\n",
       "D       0.037037\n",
       "E       0.035915\n",
       "M       0.771044\n",
       "Rare    0.037037\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cabin'] = np.where(data.Cabin.isin(['A', 'F', 'G', 'T']), 'Rare', data.Cabin)\n",
    "\n",
    "data.groupby('Cabin')['Survived'].count() / np.float(len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Cabin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Cabin\n",
       "0         0      0\n",
       "1         1      1\n",
       "2         1      0\n",
       "3         1      1\n",
       "4         0      0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets replace the letters by numbers, without any sort of order\n",
    "\n",
    "cabin_dict = {k:i for i, k in enumerate(data.Cabin.unique(), 0)} \n",
    "data.loc[:, 'Cabin'] = data.loc[:, 'Cabin'].map(cabin_dict)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's inspect how the new cabin looks like\n",
    "data.Cabin.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Optimise depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((623, 2), (268, 2))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's separate into train and test set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data[['Cabin', 'Survived']], data.Survived, test_size=0.3,\n",
    "                                                    random_state=0)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depth</th>\n",
       "      <th>roc_auc_mean</th>\n",
       "      <th>roc_auc_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.634922</td>\n",
       "      <td>0.021348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.635294</td>\n",
       "      <td>0.022767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.634844</td>\n",
       "      <td>0.020574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.637038</td>\n",
       "      <td>0.020460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   depth  roc_auc_mean  roc_auc_std\n",
       "0      1      0.634922     0.021348\n",
       "1      2      0.635294     0.022767\n",
       "2      3      0.634844     0.020574\n",
       "3      4      0.637038     0.020460"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and now we optimise a tree split on it as we did for numerical variables\n",
    "\n",
    "score_ls = []\n",
    "score_std_ls = []\n",
    "for tree_depth in [1,2,3,4]:\n",
    "    tree_model = DecisionTreeClassifier(max_depth=tree_depth)\n",
    "    scores = cross_val_score(tree_model, X_train.Cabin.to_frame(), y_train, cv=3, scoring='roc_auc')\n",
    "    score_ls.append(np.mean(scores))\n",
    "    score_std_ls.append(np.std(scores))\n",
    "    \n",
    "temp = pd.concat([pd.Series([1,2,3,4]), pd.Series(score_ls), pd.Series(score_std_ls)], axis=1)\n",
    "temp.columns = ['depth', 'roc_auc_mean', 'roc_auc_std']\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# I will proceed with depth = 2\n",
    "\n",
    "tree_model = DecisionTreeClassifier(max_depth=2)\n",
    "tree_model.fit(X_train.Cabin.to_frame(), X_train.Survived)\n",
    "X_train['Cabin_tree'] = tree_model.predict_proba(X_train.Cabin.to_frame())[:,1]\n",
    "X_test['Cabin_tree'] = tree_model.predict_proba(X_test.Cabin.to_frame())[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.6       ,  0.73684211,  0.30360934])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the output creates 3 bins instead of 4, why is that?\n",
    "X_train.Cabin_tree.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.30360934,  0.73684211,  0.6       ])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the output creates 3 bins instead of 4, why is that?\n",
    "X_test.Cabin_tree.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(\"tree_model.txt\", \"w\") as f:\n",
    "    f = export_graphviz(tree_model, out_file=f)\n",
    "\n",
    "#http://webgraphviz.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZbIBE5LLYAInIZbEBEpHLYgMkIpfFBkhELosNkIhcFhsgEbksNkAicllsgETk\nstgAichlsQESkctiAyQil8UGSEQuiw2QiFwWGyARuSw2QCJyWWyAROSy2ACJyGWxARKRy2IDJCKX\nxQZIRC6LDZCIXBYbIBG5LDZAInJZbICxaN26taycX/78+WVFRCo2wFg0adJEVs4vb968siIiFRtg\nLNgAiZIuNsBYxKsBhgVCp9PB7c5zXDp7HMOmL8evdVPKB4Hjx4/LCui79Yqs4ub4kV2y0vw7sK74\nWvHBBkhkig0wFglJgOnzVUOn4RtEPaBRJjHXNytdsnfF/Pcd18Rcr0b1r8R87969EZOe/mOzVmop\n5qrkabPIKu7YAIlMsQHGIiENsEX9CvCTdZQGKOcRDTA8GG06DdBqxcOHDyMmPV3Kj7S57h0x16td\nNH5NkA2QyBQbYCzi2wA9Ty8V82yptR+tvgF2rvK+mH/7+3Ixj5wAVZXLFZeVqfdTaJ9r6cknYq63\nf0YfWcUNGyCRKTbAWMSvAYbLuYG+AarCwgyPm2uAMdF/5IMrp2QVf2yARKbYAGORkD+BjR3bOR8z\nZsyQI406PvHglRwlHjZAIlNsgLGwtAE6EjZAIlNsgLFISAMsmTXqj1X3bmVZRe/+C39ZGbzyCxXz\nAwcOiEl19czBiDo+2ACJTLEBxiKxEmC1zrNlZdDx+2+x56YPDu3aJsYF3nlPzI0dXf67rGLHBkhk\nig0wFnFtgBnLNVD+HyDqUtm0H+s/Gy9j0i9VRZ2pQHsxj87aU0/F/J+j98Qc8BX/Vxug3gstDAop\nclcRczZAooRjA4xFXBvg6J9q4O8eLURdK0cyMd+mNK/pA7VlGWJpgO3nHxLzQPF/JRFWrYLPP/8c\nH5coqy0I1Rqk3ncfpxNzNkCihGMDjEVcG+AHWdIgY8aM6D1jt7jqituZ/1D1u9aoUPRDPHzroywr\nJJ9pXuDzGxg2eqqojU9xO+KuJcEK5WqIuffjG6hcu6GoVXWrlMLCTXHbHsgGSGSKDTAWcW2Auiz5\nMHLEMATLsSNiAyQyxQYYi8TaCZIY2ACJTLEBxsJWDdA/KExWiYcNkMgUG2AsbNUATzyK/x/LG6Yb\nzv1193xhsq3QuI4OGyCRKTbAWMS1AW686ANdsmz4vmQO+Lz0RMMef6J/syro9+cwpTmlxvlDq9H/\nj+ERjUptgHP71lOioCeClPEjf6DQ/7qIxxDmj/3790dMesuPP8DxK+5yBAz4JresgC1uXrKKHhsg\nkSk2wFjEpQHqm9rOhSOxc2AbUXeYtBHwuSFq7fHXor64eqyYqw0wef4vRa368tN3cfjmS20Q8hZT\np06NmDT6P5mjXnBBtXfaz7KKHhsgkSk2wFjENQFW6jAFGXJ9hprF8mHfhXv4YcRKpQFqV33WGuAr\neCv/Tyub5ebrfuhVpyhGjRoljv1T21oKneHq0ebk/6ITds/pK+oc772HzJkzi3rDgQuo+qXh0Jjo\nsAESmWIDjIX1tgG+lXP7YQMkMsUGGAtb7QSxBzZAIlNsgLFgAyRKutgAYxHXBrj2zDNZxUO44RL3\ndb6qgifyRiJvgoAi5eqJuslnhj29xtTtip/XMxwWo/vwC22uLM9Xro6o13etL+Z6bIBEptgAY2Hc\nAAOeuaFhM/XiBn5YsHEfSpcujh9/nSQeW3vmORr9UB8hb+6j3yjtCtAF3s8q5qqm1aqhmpwmrdit\nLTRqgCr9xV7mb7uIgtW0iyecnmq4YVJkeeR9R76q0wq6j6uLWtUmp7YzZWXH78Rcjw2QyBQbYCwi\nJ8BUYi+udoECtWXpD4FRE+DEb0uLuvg3HaBLkx0TJ07Emi1XxbLfu3RBFzmt2CXv6xGpAeqyazdF\nUj/7B2W1vboxNUCV/5PTYq4r+rWYq/SHWLMBEsWMDTAWUf8EDkff8f/JWvkByga4/twLTGmiNcDS\n33XCeym15bM2aA3KrEgN8Jv+CxH21k3USweVE/Mz0waKeWQe+utmSbpPaor5cXftmoQqNkCimLEB\nxsKmO0EiNcDY/LXhgqzihg2QKGZsgLGwaQNU0uSDBw9kbWVhgXj66o0caNgAiUyxAcbCtg0wcbEB\nEpliA4xF+fLlsWrVqgRPq1evNrvcHlP27Nnld0VEKjZAG3v48KGsiMjRsAHakH4PsTXZ4nMSuSr+\na3JC169fl1Xsnr81HBZDRKbYAG0kd27zp7BZw5gxY2RlMEIeInPbx/R6gWvPPJYVEUXGBmgD06ZN\nk5XtFCpkepvNof+dxsWt2sVTO07ba3SGymPkeu8d+D3UDshWL6uqi+W6g0Sugg3QBg4ciNt9eq1p\n2Lqzyv+1q0Y3bdIQ+Y0aYLjvM+jSvAv/uwfFMiLSsAGa0axpY3Tq2g2NGzeOuBB9XKVMmXjpyniH\nSIsxa2SlvP6Bs5EtjQ7+Sj18w0UULVAZ8H8kHtPp0iNv/k9ETeTq2ACj8Ukq7Uejv0KLo3Jz084d\nJqL4YwOMhr4BRlY0h7I80EPUkQ9JSZEihawSz+jRo2VFRPHFBhiNmBrgo6PL5chg165dskp8kXeI\nEFHcsAFGo0evXjh47gmmjPkTvw8ZLpapia9S6Q/FdsFSBd/DixCxWEiMPb9EZF1sgFbw/vvvy8p+\n1J0vaoP29PSUS4goNmyASUCePHlE81OnMmXKyKVEFBs2QAtF3hFiL/oG6Civh8gZ8F+LBe7cuSMr\nx/DJJ5+wARLFA/+1JIDa+NRGkylTJrnEdipVqhSvKUOGDKhatarZxxJrunHjhnz1RI6NDTABSpQo\nEfHnZuHCheVS0jt9OoYbQRE5EDbABNA3v8WLF8slZIwNkJwFG2ACqM0vNNTRT5KzHzZAchZO1wAn\nTJiA2bNn222aO3cu5s2bZ/ax+E5qI7UG/eepUq+3mDcZtVXMC9QbAATeFTVeX9LmkSzeGv2tNiO/\nvrdv38L0aoPmsQGSs3C6Bvjq1StZOT9rNUBVtpz5ZWVogGcfayn1pq/yv0gNsG2fkVoRHoQTJ05E\nTMYuv5CF4vn9myj0Xho5ihkbIDkLNkA7sloD9LmNk8v/lANDAzz+UDtXT+1/kRvgtOF94RWsFGG+\nItHqJ2OL+teRlcE7/6spq+ixAZKzcIkGaK7RTDsY8zF8fs9vIaa7afw5ZISslPrPobICJo4fL6vY\nWaUBhvjJwvD59A0wV5b3MadfQ1FH9ydweJj5bZnbj13CVz/0EvXecw9QKLUO925fFOPYsAGSs3CJ\nBpgQQcrkdW27NpBGrz4i5ruuvxTzz+pPxvqz2rm3Zer8iXzyCjJxbWxWS4CRNBm9TVZG3lyWhe2x\nAZKzSLINcPMNHxyc3knUOp16Q3D17z1D09lxNYbP4y93HIQbbii0csSPsoreir9aiLm9G6C9sQGS\ns0iyDfCTT4ui7zDtElXxboAKdW/nqysbtYFi+PBhGDZMm1R334qZ4PbC9ML5Jx7F7VaUbIBE9pVk\nG+D3jRqLe3p4+oahTZvW+GvArxj2z2qlbgP3MxvwU+ee8pnm3Ti+Ha8DtfqXhfu0QmrSvKX4PGOX\nHUKTZi1EPXreTiyZGb+rM1uzAfrH9+Yligue/hi8+LAcaQbUKy4r7fXpD3s5tlTbydK6Ur5YXzcb\nIDmLJNsA0+XMj4qflZYjx2RpAxw0cRa2HDyFIN+nqDNoBZp/VxN/d/oOZ5/4Y+7ogfJZwMj2jVCx\nYkUxNencVyz77682Yv5DJeP7F4fj0MyOokqvvLYytbXnCB7a9k+9FzEcB84GSM4iyTZAZ2BZA9Sy\n2Qfyc3w/bB32Lugjal3Jb5X/GyLhvCG/ipSqTn2HThTLfmtRSszb1/yfmOtdWPqLrDQPlBR83Vsp\nXhn2APdo8LmszGMDJGfBBmhH1vwTuMHIDdi/6DdR60rXVf4fl3M21NeQSlaaY/O6yCoSj+Nidnaj\ndvP1AcOiP9yHDZCcBRug8FrO48sXJXpPgo/nGTFy2yCPBwwz7D2OiUUNMMRx3wjYAMlZJLkG+OzC\nClkpKeXfC6LJ+Pr5YcysOSj+fircfO6HwvUHYemvjeD2xAe6ZFmVZ2oN8MOyTXF8seGMiuVnnol5\nSFAQAgMDtSlI25usUs+zKK40QL0vf5mpFQHu2jwWlibA2dMMX9uRsAGSs0iSCVDfWL6q0wJZZa22\nsvF9tLMiUn9QFwdHdBN1lcLq41oDXHvVX8xVpQtmw3M5fPHgnrjIpzrduqfdEziN8nkzZsyI5KnM\nnB+bSA3QUbEBkrNIcg3w1k5tI7+a06q1GoiMqXXirI4Tnr7oVucj8Zgu+XtKA+wu6qqdxsL/xQWx\nyyCt0pAqV6oglquaDl0jq+gV7DpWzJf2ryfmKu8HpoeWRCcxG2CxHCllFT8r5/8jK6B69eqo+nWz\niDo6bIDkLJJcA4yrI6NM93bagyUN0Ffek/jpQ3nWihG/t9qf7gjyhveLJ6LcPLqJmBu7+1B7LCZV\nMhle46U72rbN/zZrN4HX6QqIeWRsgOQsXLYBOoKENsBP0ph+nC59HQQ+2K/V8nPufxISUWf8qFZE\nA3z98jlG1C6DS8u1Cx3ExrgBqv5XVzvUJiZsgOQsEvYv0I7YADUbpv+h/F87GlmX+TsEexzSajMN\nMHmeChEN8L48u0Xl/fQ6nsokGZ3IDbBUDS05bzoT/Q3Y2QDJWbABGlk/7isxv75/Lnq0/lrU+6b3\nxnvZM8HdGwh7eU0sazP8PzE3Vjil6Y9y8V6tQcycNQ1rzzwSdeSGl9AGOHvqHyhaQXt95Wu3wKdF\nS2PG9BnYfOy6uGL1teNrMV0Zq5+/QAHtz9TpM2fi1INX2Dh/FOauOwj3M6uVxwqKx2Iya85cPPRS\numSoH/Ye164oM3nqNPF15i41nCttjA2QnIVTN8B5439Hx35jcXj7aqhXxWvQshUW7TbckjFXrlzY\nMmMi7ioPXtw2FdmN/nTMly9fxPRSnjShb4DmDJh/GEHPL+CqkqyuPDWKUVLXj7PJCjj7NDSiARqz\nVgOMK1t//uiwAZKzcOoGqNJl/hgTf20n6v2bViJF9h9ErdI3APWS8K17zcb27dvFsXuqYsWKRUyv\nommA7UetFfMr2+XxduHqVV4C0HnsJm1sRN8AOzVpKuarjnmJuUqn0/bAJnYDtBc2QHIWTt8Ar20c\nI7aEhXtqJ+snN9MA7ytdT1/HtMlL3wAfPXgg5t5qY3zlhrt372Lsrx2xamgjsTxVauMLCGi6f6Je\ncstgyf6nCAnQmmC9PvPF3FYN8MFdN1nFj8f9uzh13vyVouPq5MmTePnG9PJfbIDkLJy+AVpTTH8C\nm2O43EDc2KoBXt2h7hCJv6zJtK8f9kbbtqk6+8Af8LslRwb61/pF5+lIJ+vSrbTLf3UbcVDM9dgA\nyVlY519gIrJlA0xs8W+A2v0/PI8uxZMzWqqce+RJRAPMUUy9fJXWlsfvuo1ZXWuIOjr6Bqj63wcZ\nZQXkyaKeHmhK/1p1umRGtTZnAyRnFd9/gXbn2g1Q+RM/R2kcf6jthFH/9J+y7q6hAX6qNkDt/L03\n4v8xM26AqpPPDRf5O/bMfL7dfcNwKez1l56LORsgOSuna4DTp0/H4sWLk8SUkAZ4bJF2IPLu6T0x\nsEdnlKrxA0a0q4Tnb/3E55u9ZAO27TmNZEqdPJbPr2+A6sf90X9ARN25fXNR58lhSIX37t7A4u3n\nRH3/7i3M2XxU1Co2QHJWTtcAyXoyx9Ig182O2y0+uw5nAyTnxAZIVscGSM6CDZCsjg2QnAUboIMr\nU6ZMvCZ1G5655QmdEvL53NwSdlwiUWJjA0xinj/X9sxaU1hYfI94JHIObIBJiJrWbOHzz2O+CxyR\ns2IDTCJsfXxkw4ba7QSIkhI2wCSiXTvtghC2kpQOQCfSYwNMAkqXLi0r27LVn9hE9sI1OgkID4/b\nTdCt4fHjuN3zmMgZsAE6ucROZV27dpUVkfNjA3RiR45o10BMbIULF5YVkXNjA3Ri8+drl8QiooRh\nA3RSqVOnlpV9cIcIJQVci53MRx99JCv727p1KwYPHixHRM6HDdDJqMlLnW7dinrZ+sSmfy3Xr1+X\nS4icCxugE9m8eXNE06lYsaJcah8bNmyIeC3vvvuuXErkXNgAnUiaNGlEwzl79qxcYn/6JkjkjLjm\nOhFHbTRsgOSsuOa6iJYtW8rKPC8vw43ciVwFG6CLYAMkiooN0EWwARJFxQaYSC5cvSuruKvReihK\nlP9OjjRBTy+K+bMLm7S5dovgWNm7Ab5WXucNryA5Av73YZGIbYcVS5bFS49LotbvVFl68il+qVsJ\n4QGvxXIiW2ADtKEfO3XH2EXbEKzUOT/4EFOG9sXDU+sxatl+eFw/gUMeWkPwOLcM9erVk5P+wqPa\nDcjfXP1PzCOb1OkLMX9l6CkxsrQBzls0H7dvXscjt9PK6w5B/a/Lo+r/covHRvT/WcxVhu+jHjac\n8xTLGhTNLOZpI+0s6duglJirDS/02WVRG0shns/L8ZPtsAHakNIn8E+PqqLWJX8XCLyv1bIRHHus\nPEHx7OYuDBw4UE7amRWhngfE3N99n5irRi8zutua7yNsmT4ALwPkOBaWNMBnhxeJuU6nnX539HEo\nvsmgfQ/Dl51B0MNjolYZvo+BOHLzpVj2UUbtuZlTmK5unWtrDbDDV2WR0ag51v4oi5ivGdkJn+XX\naiJbMF0jyWZEAwx6oNXyH/txz1Axj463Mo1oVUIbRJJVfo73SncS89hYlgBN/84+5hmGb2UDHLn8\nHIIfnRB1dMJeaWeKpHqvmpgbaA3yx+HbxFz/VU4/034uyT7WXvP+B/5iTmRtbICJwN0B/v1a0gDv\nHV8mK6KkhQ3QhmZNHolbjxzjXhqWbgP87bffZEWUdLABughLGyBRUsQGaHch0Ok+lnU8hPvg77GT\n5ABo1qwZYtohnNgN8OL+DTj9OP73Kgl7c0N8Lyp1rq+JbIEN0OoM/+hfv/XRijClNYVqm/gDtR2/\nCAwOQ0CI9lxdSu2ubr7ecT/m7c9dN8V8/ZW3aPhZXlHHxNIG+Ob1a4SFajsn9Aem+PsHwdtb3VVj\n3vkn2vf35o12SA9ibNGa1v1nyUoT+vKMrIisjw3QyhYcdMe9E5dQ86NMconyQ5Z7bPe7h2LHPG1b\n2sxDHsr/te2DagM8OL270lD8xXNzFm+gLI25Weh0qcR88ibtuoBHVowS8+hYIwHe9HiJzHmq4uza\nIWJcvuEU5f9aA0xpdBjLsOblxFxtgLqcZZXvKwBjt3ng4D0vXL6wXzwWk6xGh8tkLRR5zzGR9bAB\n2oDaxMq9m1yODA3woHsAtswaIOolJw23l1Qb4NCW2o2GAv3eiPnHObQGF5N/2leSVeys0QBrdpuE\nTEUa4dQarQFWaTJb+b/WAFMZNcBnp9XlsgHqUoj63h214Rt+FjHy0s4KIbI1NkAra1W/NlbtvYID\ny4bhsRLiVh68hWHDhuHwjjUYMnQEhg4ZgsuefpiwZBUate8rPmbYsKFinieLlhp//fVnNGzXX9TR\nmTp7oayA6f8MQ8xHFFreAIt/VFDMq5UoAPi5IzzMF0P+GoJ/Ro/AlDlrxfeoGvtHK+w6/xAX963G\n38NGimUZc+QR8wa1v8Tey9rZIdHpZ7S3OcxLO3CcyFbYAO0gONAfP43fIEeJIzF2grx+eAUvvf3k\niMjxsQG6iMRogETOhg3QRgJ99Xs+48fPxws3btyQo4S5ffMGXnuZ7p21VgN8dP+qrOLn1asX8Hzu\nK0cJo/5c5I5zIqtgA7SV8BeyiJ8OtT8Q87mrDin/1w44qV0wnZjnrdlVzM0Z0LeXmL/3gXZIjS7n\np2KuZ60GOLJlAo5ZVOjSvCcroPu4VWLuftiwHTOy2rW/EfP+s7bD7+k13PLWOt85BlWyIjZAK4g4\nYCVM24Nbs9O8iAao3+upXrTl3Y9rwffWLjGOjr4BqlpVLSorIFvuQrIy1a9TAzyVxxYas7QBnl6i\n7a0e0r0FJrXRrtqifg/6Btjhr90IvLFG1A+VBwqkj3lVMtcAVdolr0xtntATzw6YP/+YDZCsiQ3Q\nCtQm13P0clFffeiFys3Gmm2Avy2/IGo9X1/fiEnPuAGq+v53Co0/zSDqDMX01wo0lbKotjz40REx\nV1kjAfaYdVArwrXGrn5Hxg3Q+7bWACMz931F1wBV6vUSjak/MzFl0g7zWXLotpir2ADJmtgArSDc\ny03MfW7txbSpU5ClSDncO7MeL9/6iH/IfX7/Gwu27kapfBmQ+31DIzBH3wDVjxs7pLeog59fw5s3\nb3D7bTj6tKohlqkKpdZh5oyJcgRkKVRHVtZpgJ9l1VaPrwqlxa99fkXvfn+i2nspEBwWDl3yHFi2\neAzO3XwoXmuGVGnEc6Ojb4BBAX4oUqebqNWPG9RvoKgLFCou5npPds8Xc9EIlWnUxmtizAZI1sQG\n6GA61H5fVuY9uXtFVjHT5TD8+ayy1jbAhNKlySUr84YN6CmrmLEBkjWxAboIezdAIkfEBugi2ACJ\nomIDTAD9dilnmtq3by9fvXlqAzT3cY4+EVmCaxARuSw2QCJyWWyAROSy2ACJyGWxARKRy2IDJCKX\nxQZIRC6LDZCIXBYbIBG5LDZAInJZbIBE5LLYAInIZbEBEpHLYgMkIpfFBkhELosNkIhcFhsgEbks\nNkAicllsgETkstgAichlsQESkctiAyQil8UGSEQuiw2QiFwWGyARuSw2QCJyWWyAROSy2ACJyGWx\nARKRy2IDJCKXxQZIRC6LDZCIXBYbIBG5LDZAInJZbIBE5LLYAInIZbEBEpHLYgMkIpfFBkhELosN\nkIhcFhsgEbksNkAicllsgETkstgAichlsQESkctiAyQil8UGSERERORiGACJiIiIXAwDIBEREZGL\nYQAkIiIicjEMgEREREQuhgGQiIiIyMUwABIRERG5GAZAIiIiIhfDAEhERETkYhgAiYiIiFwMAyBZ\n5N69e0iTJg10Oh0nB5syZMiAgIAA+ZsiIiIyYAAki9y9exdNmjSRI3IkefPmZQAkIiKzGADJIgyA\njosBkIiIosMASBZxhAAYGhqMZ56P8OjRI7nEjPAQ8fjzV14IDQuXC5M2BkAiIooOAyBZJDEC4IsL\n28QxbUFyfO/4Ihy45SNHwIBGmXDySZgcAXN6VseO2/6ivr1tDPosOiFq1b7JbfH7jmtyFJMAdPjh\nK3gGxj0s1syTWvkozfhW5XDEQ3sNkfWesVtWtsUASERE0WEAJIsk5hbAbMl0KPBlFzkyMA2AQSIs\nGpiOowuAFw5swBdffImL91/LJfET8vAQdMnyypESHx8cUb5uLjkyCA8Lw/WzB1EsT3roMn8kl9oG\nAyAREUWHAZAsklgBcHr/7xGozKd0/Qrpc5XXFkqRtwCaBMCgR8o4ixzEvgVw7ZyRqPB1EzzyDhbj\n4AAfeHp6mpmeiMcjhL5Svk46OQDun1iItMUbypF5PjdW4c8lp+TI+hgAiYgoOgyAZBFbBkB1a9m6\nWcOgS13GcNxeeDiWj2gpQt7inWehLo0cAFWFStRCUJA/SpesLJdo4r4LOAECHqFGu2EIePUQFb7v\nLhcCvzb7CqfdtS2L6useOHIqpk4YhZP33opltsIASERE0WEAJIs4wkkgAxplxMU4ZqmjMzrYLgA6\nGAZAIiKKDgMgWcQRAiCZxwBIRETRYQAkizAAOi4GQCIiig4DIFkkKQbAj3O8I+avLq7H0FXnRB0d\n9Zi+PTe1S9KEv76Id/IWRrFixcT0wTuZcD8if/khT2FtefHPv5PLAI+bbvinQwXU6DlJLrEeBkAi\nIooOAyBZxBYBsM/3VXD+1l10bVARf48Yi2kLt+PigTVIm0IJW/eCcfXQJqRPkRzbrz9GkfcyiRD2\nWj1pN+A1vqv0EVJna6p9IiOf5M6N3GamyeuOy2dofiyRCWM2n5cjoFgyHY4/1s4INnZ1+2Q8CQIa\nFzMEQBPhb/Htb//KQQDSp0snXme+EtXkMoOjy39HzZ4T5ch6GACJiCg6DIBkEWsHwOCnJ5H8vYpy\npG5hSyMroFaOZDjkoYWx70plwjYZvKYPbIFmc3aK2uvcJmQo0F7UxkoXKYIiZqYZG0/KZ2i+zpoC\nS464yxFQIrkOO9y85UjT6PtWsgKaFtfhoLv+EtUGH2T7QFZRpVeCoDEGQCIiSmwMgGQRq28BDH6O\n6gOWy4GpakoAPO6pXQ6mbqkM2O2uhZvZf7ZBi7l7RO19YQsyFewk6oQJhi7rp7L2x3vl28naPHUL\n4N5bvnKkWdCzprhmoV6Qzwu80ucw/ycYs8JwZxLVydV/4ste3AVMRESJhwGQLGLtALh9xu+yMmjd\n/hdZUXwwABIRUXQYAMkitjkJJBw3btzAa2/j7WgUXwyAREQUHQZAsohtAiBZAwMgERFFhwGQLOJM\nAfDG+eNIrdPhxKOoZ/XaSshLd7Rr3RrjZyyWS4DvviyDlOkyYcq/O+QSTYtan0GnS46Z6w7JJZZh\nACQiougwAJJFrBkAp02ciMdv/fDshZ9cEoIpyrJTVwxn5SLYH8euqmN/LFqyUlumWLF0kfJsg307\nNon5soWL4CtvI6yqld80AD65cwFTps+VI83RzUuxbs9JeFz3kEvi79XV7UiWtbgcSeE+CDC6ZfH6\nyV3x3egVcmSQOdJZwgnFAEhERNFhACSLWCsAqtfIe+IdKmpvbyW0BL1Gu9+1YDbzx5po3W+rqJeN\naInO07eLGn63lY8rrdUK9XNoXit1alkDI9qUQcO/tWvyGQdAXfb/Yfv27WLKrXxs31VX0K1aJkz8\n77B43OvJazFPCPW1vPDXIum6YS3w2Y8jRW0QhvI/jpC15tLJPejWtp742MPuppeeSQgGQCIiig4D\nIFnE2ruAV47tggJf/oSHB+fjqz+WimXb+rVC6wFa6Fs2vAU6TNooavjcgC6ZYStbdAHwwIL++G3R\nQVEbAmC40fM1+4/fRHiYtoku4IlblMfjI4vyscaXh9bp0ssK6NqyhawUYT7KKzEVfHczBq40XIw6\noRgAiYgoOgyAZBFrBcAzly+hW6tmGD13g1wCjBn0M7advKuksYcYOfVfIOQN3G7dwe0b1/DaNwDn\nLlyGu7s7zt64h8vnz4j63NmLyke+VYJhBhxdPR1NOvSICFjP797AXeU51y5dQJBcuG/jEnTqMRD6\nSzlfPLMH88f9jYbtesglljl16pSslKwX4C2+vvo69dMbf/mVw/xx+9ZN+Frx8EQGQCIiig4DIFnE\n8U4CCceicX3F1ruN200vuOxqGACJiCg6DIBkEccLgKTHAEhERNFhACSLWDMABvm8xPC/fsXmc0/l\nElvzw8yZszB79mycufZCLgPCwyMflafx93qNNz7RBKpoPiYh9MchRifc31O85lmzZuHWc/0Z01Ex\nABIRUXQYAMki1t4C2KlyRqw980yObCz8CTIV/FwOgBO7tsgKmD/gO/w2fZs2CHRHZqN7AlcukhH+\nsladnTYQVZuZXkrGnOeXt8Jw2+BQpNDpcN/kpsEv8OeY8dB9XF0Mg19exh43L1GrMqTUYdctw1de\n2fE7LDnzRI6iYgAkIqLoMACSRWILgD5uG5Dp46/lCOjZsYuYN8uvhBk37TIp6vF6+u1nxgFwcv1y\nGLH4qqgfHZuA4t90EHWtwtnEXFUigw6zj9yXo3iKFABVoQFv8Gvr75Aix4d4a7whLsxLvM4PKhmC\noN7pqQPiFAAje7dca1kBM/toP8NgjxMRATCylDlKyErDAEhERAnFAEgWicsWwM1j26JK58lY/8+v\ncglwZ+8C9Ju3X9TRBcCJ35aOCIB3D4+JCIDGl3ixiJkAaEz/dYKfX0LRxgNErfowU3KceGo4XTe+\nAXDdlH4wvsJg/UqFxc/AdMonHwUubp4Ct9dRdwszABIRUUIxAJJF4roLuG3ZrCZ36mhR5gPo0r6D\ndoMmIJMSeMp/0xbPrp1DOqUuXrcjXgUBL67vEGGoZNnPcWH/WKXOjKPXPRD04rohKGUx3SoWL5EC\n4Jx+DcTnrFGnIf6cqF04Wm/bzMEYNWUBtmxci6krtGsKql4/uIxGlZUAl/Vj7DtyUyx7eWWn+DxP\n/E2PC9w1a4DhdcvpxBPjn4oSNh8chS5PeVG7HZgd5fkLjj4Sj6kYAImIKKEYAMki1j4GMFGFeyJ1\nnjJyYD0B3q8xZNBYObKdtR3rYOEpTzmKigGQiIiiwwBIFnHqAJjEMQASEVF0GADJIgyAjosBkIiI\nosMASBZ58+YNhg4dardp2LBh4ti4kSNHmn3cXtPw4cNRqFAhtG/f3uzjiTWFhJgeY0hERKRiACSn\ndfXqVRQrVkyOHNM///yDwYMHyxEREZFjYAAkp6Ru+fvrr7/kyLG5u7ujcOHCckRERGR/DIDkdNSt\nfpcuXZIj56Huqo7JssULxXOyvJtb7D5WJ3VsuOKgKc/bV1EknQ5rzzyWS4iIiOKGAZCcihqIQkND\n5cj5xBYCm76bAcPWnZUjhdGlBG+e3I3efX7DvVeGEzs61EprEgCH9OmNwxfu4cLDl3IJsGfDAgwc\nOU2OiIiIGADJicQWnpxF8eLF4ebmJkemmr6bEZ1GLcSxY8ewdEwveMsAeHX3Etx4E47wED/kUH4O\nj+WNQYwDYEpl+RG3h6L28g8Cgh6jSPkOWL58uZjUn9+pR84bnomIyHoYAMnhvXjxApkzZ5ajpGH0\n6NFmj2FsFnkLoPR1LkPoy6cEOU+jALhOBsAwme2u7p0HXdbqSgB8oIS+9NpC6ZKnt6yIiMiVMQBS\nnPzxS1tZAZ+m0mH7C61+eGIhPG14pZGVK1fihx9+kKOkRT05RD3OT8/97l14e3vj7asX8HwZOaiF\n4++/horq3J5VuPMqCAFvn+ONlzdePvNEUDhw785j/DP8d0xfuVU8T2//uiWYOHe5HBERETEAUgJ8\nogTAVTe95AhYMnWE2L34d8+2SKXMDx3cHrG79uap46I+/0oMMbbbV1i79wS61yuOsvX7agujUa9e\nPREAk7qksmubiIicB995KN4iB0BV5BBjPP7fOzpceg3c2DsTvf85KO5OEdsdKjJkyCB2/boKhkAi\nIkpMfNeheCuYXIf/bpkGOHMBUH1GyJuHyKQExhuB6tIwsbx1v4l49eoV+rdvoC6MwlXDUJEiRXDl\nyhU5IiIish0GQHIY6uVdXH1L2JAhQzBo0CBRq7dxy549u6iJiIisiQGQHMLZs2dRtGhROXJdhw8f\nRvLkyUUQ1k8TJ06UjxIREVkHAyDZ3e+//44//vhDjlzXihUrTIKf8URERGRNfGchu1KPe7tw4YIc\nkV7krYC3bt2SjxAREVmOAZASlT7QHDhwQMyd+bZuiaFLly7i51S4cGG5hIiIyHIMgJRohg0bFhEA\njSf1en+BgeI0Yaejvv7g4GCbT1evXoWPj4/Zx1xh+uWXX3D69Gn5UyciIksxAFKiiRz81KlNmzby\nUeekfg9kez179mQAJCKyIr57UaK4efOmSfBzc3OTjzg3BsDEwQBIRGRdfPeysb1794qzO115WrVq\nFfLnz49UqVJh8+bNZp9jj2nNmjXyt5Rw1giAE0f/Lj5PihSpMHHWBrHs+sm1yJclJQqX/xburwJx\nc/cf+LLPQjx9+hQh4eIpuHFoNabMmoVf+w7WFijUx5+67YHui5/kEuv7snRh5fWmwOLtF+USM/zc\nxfekTgWqdpQLE44BkIjIuhgAbaxUqVLirhfkeKwR3qzxOfR2T/tZ+XzviLpSkRJirnfnwAg0GbVV\njoB2FXJiyvFHchTpdby+hNTf9pAD85ZOG4Y6DZrilZ92Eo560WlzU0x0upSyiiTwATLnLISZy7fJ\nBZZjACQisi4GQBtjAHRcjhYAVXcOzhGf0+2l3MwnRQ6AyZXn3PKVA0V2ZXzqtfyYaAJghx8qY9D4\nRXKUcMcO7kbnFl9pr/NNzCFxYMvyqPnzXDlKOAZAIiLrYgC0MQZAx+VoAbDa9wNlBVT6IAO+7DpF\njqIGwJ2TO2DAopNyFAZdsvdkrYjDFkDVsZ3L8Vnlmrj93PS+znF1d99kLD79RI6i8xrDN1h+nUcG\nQCIi62IAtLGkFgBfXliPHnOPivrHsu/DW1Tm3dg+1SQgDWjwMZq264K2jb8Wy4/c1T56eOtyaPRj\nZ7RvVlcs33njtVj+QvlaY7bcFPX1TcPRZfwWUVuLYwTAMFy/cQuPHz/GlVsP5DLg8lU3sczjvjse\nP/eLEgD1rl4ycxxeHANggoT448GD+4h89cb7Z7di4xHtd6V66GH4XqyBAZCIyLoYAG3M2gEwPOAR\nSnynvrmH44MsWdF70FDcfeaDv3o0U8JIVvGcMQPbKnUyLBz2Mz4rWQi6LJ+K5RcObBGBZcvll2Ks\n53/7GD777DOzk6kA08ATcEd8HXNqV28k5tEFpO1jO2HxScMxbHqHp/fGxH13RZ1Z+djHRnsYUytj\nj5j3OMaLYwTAuHl8aQVadOyFoUOH4kUMl0xUHx/6Z380+GumXJI0MAASEVkXA6CNWTsAjmpWCiNX\na1t9js5oh9+XnBU14K2EkeyyDjYJJsZ104o67Lhq+nr8bhwQZ+mam0z5mgYeb7coASjwhRv+WXMS\n7u7uYlIfV+fG5vzaXHmFUS39sw38ZK3nceUY5i1eAc+rO013c1pB5NeeENb4HBQ7BkAiIuviu5eN\nWTsAjmhUCp5yC1BwUJBWCH5KGNHOIAVCTYKJcd24lA777xidPRBPN7eNxz/btUDXrVJuPDWX5IwY\nf+3L26aiVe+RGD9+vJiqlisvlt/etxgNf/4zYnnNcuXEcr12VQpjmdHuRWtxxAC4d+EQ/Dwmtl3d\ngchdoJCsEy7M/y1847hFNSgk8k7fcAQp6586BYeanrCiCgo0XjdVYcp/CccASERkXdZ996IorB0A\ny1ZpLCsp2AOHHiQ80LkyRwyAiSLgMXS5qskBkEv5HqK7I/ODE/9i6sgOaNBzoVwC3Nk7VVZmBD7E\nkLGToMvWQAx97x/G5eeGgKjuxr/wPP5RkAGQiMi6GABtLKmdBJKU2DMAGn+ccT33j1ZoOGydqPfN\n74D8FbRjKT2PzUbmclqoUul0yWUVfz2/z4MWQw0nlAz6tgza/RX18jANv/pWzA/M7GgSAPXafPUp\ndJkKyhEwoaf2WoNuH4wIgJFl/KiOrOKHAZCIyLoYAG2MAdBx2TMArhzZEd0mrcb1UzvQqJ/hOnkb\nh3XE9zIA7l3QB9/2107m8L1zCLqSWiATl32J5uSbuDi/YhAqtxgiR8CXRVJizz3Toy/zp0kuvjfj\nqdjXUe/bvLBzbbwKB7p9kjvK83W6AvJZwO75g/E0hpNXYsMASERkXQyANuZMAfDa2e3ijTsx3Tk4\nC7naj5QjIH2kEFG6jfZYwBtPjOjdKuKMZmuwxvea0M9R7L10Jt9ngx4TEObzFGXzpkGGvJ/B8/kj\n1CySFclyFMfNR88wsE0t5XnJcez6YyydMkB8zPLN+usAxl/A47MYPG4uRvTranTijb/4vJFz2t6p\nP6JOVy2khnrfQRrlOcXLV8XwqeYvKh10az90abUtfcf+GxPxPeqnbTd9xGPxwQBIRGRdDIA2Zq0A\nuH/FdHzbrB2O7t4hlwDbVk5Fra+/weHLHmIcFuCDzcsmQz0icPWs0cobvLbb7vbJTWjStruoVW6X\nTmHSvBO4eXSDsvwXGI7Qei3eoA1C0KNtE/w2epocK0K8Ub9WLazedQwXHr6VCzWeN6/j3LlzUaaL\n12/JZ5j6qkZdnF07BsV7T5JLTBXOoV3WJkKAuxIs/icHljP9XhMmIZ9j//RfkO3DMjh65jKuXbuK\neeP6Y8aW8/JRMocBkIjIuhgAbcwaAdDz1FLk+KSqEscMPC7uwnNlwevnD5A+mQ5P5FH8+ZRAov9q\nE39rjJ/+PSLqoPv7kep9bRfiwRHdUL+btptRlUX5mFvimsyGALh3xi/oPHwSJk+ejCkThyJjxgxi\neTLl8S1Hr4g6JMz07M8AH2+8efMmyvTW23SLT+jLa1h34bmoD68YiRK/Gu54obd7Sic8inyGcRIJ\ngBR/DIBERNbFdy8bs0YA9Hv7VFZApbxpcNoLKJNJp8Q1TTYlhDyVJ1aqAfCZVmJ8n4Zos3i/qAPc\n9yL1B/VErQbAel3WiFr1YQqdDJeGAHhs6V/IWqiWqFV+jy+Jy3gEyBR6//AyJC/UTBtY4NyG8Sg1\ncI4cSeFv0PhvQ0CNoAbAdEXlwHIMgM6DAZCIyLr47mVj1giAvm884f/qPmbPni2XaFav1kKc5/WT\nYrdvUIC/GIcGByIs1LD5LDQ0DCEyIAYGh4gA+EOPzdiyehFOuBnuxhEQpKU7f3/DvWH3bl2PK3f1\n93sNh38gsH7FQhy5ck8uSyzhymvXvgk/P+37tFRSDYDb/mmGtkPWypFtqduAS6TX4cwz063B68Z2\nwpo1a8R06qa2/hxdPAA/9R+LNauWiuMIP285VCyPCwZAIiLrYgC0MUc7CeTZ7QOo/Glu5PiwJI64\nPZZLXVNiB8Ap3b7DSeVnfmrHclRpPk8sK54jFS57vMDwtl+gUucJSlr3wV9daiNd0aro37EtujSr\nBV3m4pg5pAP6D+gvvp4a7XetXizqsX/1wYDfB4r67DMtuG8e3SQiAIa9dUfN5n/g5VP1tn063H2j\n/DHw0g3f/zYLL57fR2pdevE8Y927dzc7nb1rfj2uksk0AL68fRy9u/0kvl6yd8xfsDr85XlU6hB1\n1390GACJiKyLAdDGHC0AkoEaUCwVn88R4HlePL/w57WN7ooRgv4Dh+HxtYNK0PtOLAn2OIQUhb4Q\ntcr4a5TMlRL7n2hbatXlERdwDn2sjLVb9xkHQPWYTXM++zCr8vz0OHI96h8BNWrUMDvtu2o4FMFY\n5AAYmf516V3fNhOXX0f/fHMYAImIrIsB0MasGQD/mz0auTKnkSPbO7l5BHr0G4xhw4YZgkaYPy5c\n0E4CMRHiqyzX7lEc2d69e/HkjWG3cuxC8No7mnvMhXrhhZfpY9cuXYCXPDZRtX7JFPGaGzZsKJeY\nl9gBsHX772WlflxqIOgGdMkzifHsIW2RPNMXuHNL+dk+2o4URaqL5Srjr1H6gxQ48kKr1eVu4uQd\nYHG/2lh/RXtg+8jmaD5ouag3jGylPC8FNh84iYObl2De7ms4u2lgxAlFf9X5AA8tuD6fqlo2Hc7K\n16Sa0LcRTrhpYXH0gF/EXOXtfhw585cSvxd1+qLkh4jrWsEASERkXQyANmbtLYDWCC1xtX7cV5i2\n774cAedvG7YWva+8Du3NOxwj5+8VlWpCpxoo1OZPUY/u8g3+PaY/flB57cmyyypm3T7OhjXHo/7M\nhrSqh7+rfYTFez3F2MPNEAhu/TcOFX+dL0ea2H5W1vhZJubvIzL1a0d3C7ekhgGQiMi6GABtLLYA\n2KJUdiw+LoNVyFPsua3d1zciWDw8Bd07xluODL8y47pAeh1uqh8a9gZfdjWcVas+x7C7MX4iB0DV\nmU3/it2K2fOWkktMlXgnraw0vvf2idcw5r+zcknsupoJgC0HLhDz30p/EBEAheC3aFqjjPgaG89q\n10PUM/75mBPb43Fhjc9BsWMAJCKyLr572VhctgDmTK5duqXflM3aAkXBLOnEPPJ9VY0Dh3GdXwbA\nS1umo2K7UXKpJjCBm4nMBUA9n8vbUM/oFmaBHqew7NBtOdL0qZUdV+QuSiA0zmEpcgAsWaI4Chcu\nLKasaVLi3Q8KYNKac/JRg+SRPn9sX88a4c0an4NixwBIRGRdfPeysbjuAs5d7kdZabIqwSJZ2my4\ncf+GCBlbzt3G+D+6I0eOHBgyTgtef7YsJx7rOXk78mTU4Yf2/cQuwVOrJ4jl6rThvPkAFxeRA2C7\n+l8iU7acaNetN7z1x/AHvUKGDBmRPVs2ZJNT1Y5j5YPAlCE9MXbcOMwYP0EuATpWVm+DllmOTO1e\nOR0F3s+Jz2o3x6W7EekxwpAaxbDqkHZ82Y2jq5AxdWqUqVIdizZoF7w2Fls4i+3xuLDG51CFhwbj\nwLzuqNisj1xiexnMvPbquQy3bHP31v5yOLluHDKmTS2Wzdtj5vjPyN7cRr9xS+Fx+wJyf1JBLlT4\neaDXyHnwuHsFuYqUkws19w9MQOdhB+QoKgZAIiLrYgC0MWsfA5iYVg0vj2HrLuLtW9Nbvlnq2JZV\nOOfhJUfW5+frLV5zbOHMGuEtps8xa+ZMLF68GDtOabfCm79gAWbPXyrqjfNH48MPP0TXfob7IF/d\n8QcqNlUCoN8TzJk7B2s3a6F23pzZyudZJmrVzRPbUKLwh2jbZ4RcYnDn/EZMnDgx6jTF9PhIVdZk\npq/9q7wplO8nJXoPnSGXRPYcjf5eKevoeCufw+jSMiHq2ckZRanTpRJzIfylMk4mB8CVraPQbcRB\nOYqKAZCIyLoYAG3MmQNgUmfrAKiqnS8NNl97I+off+ol5kAwPN6G4fGtK2jw8buYsu6uWBoRABWv\nLq5Bjk/biBrwj/g6TT7Kjr///gt//fUX+vX4CZ9XrCaW6z33uCjOuo4y7T8qn2EQOQAaK5QuGa54\nmx49unLiIPE6Yjtz1/P8ZuQtXBFHTp/DoHbfIm2hGmL5C7c9yJunDA6fOIdhPzdAsvcMWwcZAImI\nEhcDoI0xADquxAiAqszKc7p16ixHwOh2VTD5sBb6Rtf8BFPXa3dVubrzD1Ru9quoX11cjXeKtRY1\nEKR8HW1r2agmJVC6fm9Rq7YtjnQbvXiIKQD+0vhLWZmq8Z52T+i42D7pF9TrM1GODA7PG4gvOo2W\nIw0DIBFR4mIAtLHt27dj4cKFnBxwWrbMsFs1oRIaIj0fuMsqDAHRnaYd6os3/uqDYfAO0hbp3XfX\nf3zCxRQA4+Lh4yco98NPcmQZBkAiosTFAEhkAWtsRbSXY4f2YsOGDdi4w3Adx8QXIl7Dpi3bcPOe\ntqvcHAZAIiLrYgAksoAzB0BnwgBIRGRdfPcisoAaAMPDwznZeOrRowcDIBGRFTEAEjmwkJAQh9/K\nOHToUPTt21eOiIjIGTAAEjmojRs3olatWnLk2O7du4d33nlHjoiIyNExABI5oG+++UYEQGfDYyKJ\niJwDuzWRg0mWLBn8/f3lyPmkTJkSXl62u9MLERFZjgGQyIEklS1odevWxcqVsd02joiI7IUBkMgB\n3L59G3nz5pWjpGH37t2oVs30VnVEROQYGACJ7Gzw4MEYOHCgHCUtwcHBPC6QiMgBsTMT2VHu3Llx\n69YtOUq6GAKJiBwLuzKRnbhaKCpUqBAuXrwoR0REZE8MgESJzNfXV5wp64rGjRuHbt26yREREdkL\nAyCRDYWFhclK8++//6JBgwZy5JqePHmCzJkzyxEREdkDAyCRDeXJk0fs6lWnfPny4eTJk/IRUn8m\nyZMnj/j5EBFR4mHXJbIhfbiJPNWuXVs+w7WoxwCa+3mo06BBg+SziIjI1hgAiWykYcOGUULOsGHD\n5KOu7enTp1F+NupERESJgx2XyEaMg01QUJBcSpFVr1494ue0ZcsWuZSIiGyJAZASRZYsWcTxXup9\nbl1hUsNMhgwZkC5dOrOP22pSv26TJk3kTz1h1Pv4qp/H3Oe31aSuG9myZRNf11XWE/3xj0RE9sDu\nQ4lCDYBke25ubmjZsqUcJYwaAMuUKSNHZEtp06aVFRFR4mIApETBAJg4GACdCwMgEdkLAyAlikQL\ngAEv8FmVenIQvV5Nq+HqkwA5SjhvHz9ZxZ+3t7esrMdRA2D/5tVw/pm/HJkX8OIGKtTtJUcJF+Tv\nI6v4C/DzQbDppRvNCJdzpQoOgG9Awo/vZAAkInthAKREkdS2AOZJafins2FUO4zZ7iZHUTX6uT90\nuoxyBBQ2Pu7r9VEU7TxBDiznylsAH59Zjgo9Z8uR0tx06WVlcGDTWlkB/3SsiKHLDova69pm1Pl1\noahVueTv99DqGWKu91PlArLyRbJ01WUNDKiZHQ9C5CAeGACJyF4YAClRWDsA1i6QBk/khpg2n6bF\n1hu+2iD8JnQpP5D1QyUEZMALsYEm1OSA+wIZdDj2OAHv2Iqg+7ugSy6/hiLM86zyuXPKkcHtfbPh\nLl5WgHgdxnrUKydej2FbknXYMwAGPTuNrEU6a4PAO9ClL6LVitLv6nD0caioKyk/+18n/yfqtmVy\nYeKmm6IOvncAukLfijohCqXRYb2bYetfNmW87fJbOTII8X+L3zs2RKYCJWG87c770QXxO6nTa6pc\nYur5ibnYfst4i68P0ijPz/Zxwq/pyABIRPbCAEiJwtoB8PHZHShQuRHevn2FvLn/J5cqgj2UcPau\nVgfeV97Qi2q1wjgA1siW8ACofBHlcyWTNeBxYBJyVWgjR5r1vZuIrxd5UmWXc9WRJX8gyydN5chy\n9t4CWDpPRly+/wIrx3bB5vOP5FKgYoFkEQHwGyUAnvTQ9rNO7lgGw5edEXWQxzHoCtYRdUL0qJYb\nM7felSMgmfJzjnmns7pOZBPzx6eXoO3wraJWpVI+1ivSruCPvzLaPR38FMk+Mfycy+RKiY2X3shR\n3DEAEpG9MABSorB2AOzXoKxJsMpT8huxfO6wTmJ81u0uFo3oIuqjZy9hz5p5ol65/QDu39C29DTp\nM058TELVq9cKuzauxMgle+QSIK/yef894ylHev7i60UIeYPq3zXHzp07MfCXn+VC67BnAAx4GvUu\nH0fv+cLj5nFRN+w1Dg/cLom60a/j8fLxfXyiBPFcnzdEYEg4hnWuLR67cPeZ/Izxt3PWH1izZSe6\n/NhYLgHu7loAXbpPRT38xwria9Rt2BIDxhh2+aqGdK6PBWs2YdvGf7H0gOku/ZLvppOVwcGlIzB0\nykLs27cT/cctlUvjhwGQiOyFAZAShVUDoM815U08G7YfOY+nT5/A7exBVG/WQz7o2uwZAEtm16FN\n/3/w+MkTPH78EL92aoR73rGeUeHSGACJyF4YAClRJLWTQByVK58E4owYAInIXhgAKVEwACYOBkDn\nwgBIRPbCAEiJwuEDoK8bdClLy4Et+ePDip1krfwDTJNPVgrvW+gzajKmTp2KqfNXyYXx4yoBsEpB\nHc7rTwO3sfrVv9B+J8r0OlguK5M/YtnUadOR4b3PtQfiiQGQiOyFAZASRUIDoJ/HKTQbNBc3Lh6F\nTpdHLNs4tgu6jl2Fw+unQpdKuy7bvzNGiYP7N8yfhBatmon6xoVdaNqsuaj3XHuDO6f2Ip1Sj543\nD42btRBniXafukN8PN5eNQmAxQv8D243buKjjMrz15wUyz7I/T/cunULNT7MAi+xxGDsz+3QsGHD\nKNNvI0wvKTLux+roMGKLHAF/VSuBXlPUa9MFod2PrZEhmXbyhGcCry9t7wBYudC7uKn8jPq3qIS9\n7tqFtrPqkuHGzVso+04KTN9yDX7P7uCL/2VG9cZT0Lh+Q5T5MDtKNR6KX1p/j9aNvlG+/xzi46b+\n01epU2FQ96Zo3eRb8XOR+QuVChgCoNvWGRg8dx12/6esDynfE8s8j/+LgXM34+Kx7dC9X0osi+D3\n2OzvSp30n19vwbgBqFG+uPja9Xv9I5ea+q2u0Vno8cQASET2wgBIiSLhWwCDkCO1DulyFsLD14ar\ntk0bOQSnL6kngxhWYeO63kc6HJQBZMusAagweL6oO9TKj8UnDJcnUT9GxAijAPhrtUy4Y+YmIQNb\nVxPPn7h8t1xi8Fv9GihdunSUqUWPIfIZpjZt2gTPtz7i85m7GE3BTMllFT/2DoBrJvYR31Pvv6bJ\nJYqw1xgydgL2/vsnvugySiw6N3MQqjTRX7TZW/kYw0Wb1Uuw6H/8xr/TJ2dnI2957S4vhgD4GrpP\nfxDLTAR5ic/zbqGyMFptNL73zf6u1Cmme3r43dyCSj1Nzxz2v78HM45GPus77hgAicheGAApUSQ0\nAG6a85OsgB8/0UG9kpwaCl4p8yfXDsqAoMUF47DwQxEdTjzW6j0LB6PSwMWi7vRNQXSZulPU8LuF\nvLV+1erA29Al/0iUAQ9Pis81dcV2PHjgjvrNewLhz7H+krbdL8x9C36aJLccJtD6Sb+g1eAFcgQ8\nvboVc9YdEfXVwxtx4Lb6HcafvQPgwHkHZOWLd7/qgTvrRiFzka/Ekl5NKqJ0h7Fwv3MfJ2f2x5ct\ntFAOJQJHDoB6xr/Ttp9lg7ufttXvi/w6nHspSpTNmRI5/1cZ9x88wMpJA/DIF5jcu7v2oCJf+oRf\ncPunnzqKdU59jR26ynXFSK7SjWSVMAyARGQvDICUKBzlGMAOtQqYbAFMapLaMYDGATApYgAkInth\nAKRE4fAngSQRrnISSFLBAEhE9sIASImCATBxMAA6FwZAIrIXBkBKFNYMgE/unYAuuXa2Z2JoW+Md\nuBudHeD/5AoypU0hdk/2GL9GLlWEeaF+qz64cuUKunxfDXE5kTfgvnYcozrlrdVbLPO9uS1imX6S\nh74JusxFZBWVowVAP++36FThXUxcf1UusbVA6JJll7Xm6rEjKFfwPbQfvkguMUhh9DMOjuOBgp6n\n1yDVu+3kCNi/eGjE5+g97j+5VPNrpbQ4G/mUcSMMgERkLwyAlChiCoA716zBjh07sHbDXjE+v3Mr\nNq5XL40CPL1zChVKF0fLroPEWAh/obzZapcKWbPmP/Gxql1btM9juPlYINo2qoEaDdsbLdOE+DzB\nqlWrzE6Rdaj9Aa7IEw4iy/5xbVkBk9pXQLmGv4l6x8Sf8N+5p6KOjs/dfSI0VKjTIuKs18jCX17E\ngOUX5UjlC11O7b625tg6AAa8uo/1G7dg2+aNeK2+6HB/bNy0GYdOuovHJw7tieLFimH17gtirBrZ\n8mMRAJ+6n8CGTVtw9fYL5Rfgjf/WrceBY6fls4DdK6eLj125+7xcYrB77Wqzv6vDZyMHSyUApon6\nx8HK0bXQfZzp7zaz8rPPnrsQVu83/vnG7Nua3yJICYDvFdfCur/vazHX+/Kj5Lj61pAke1XJhHMM\ngETkgBgAKVHEtgVww8jmqN1XOyt2wd8/i7lq2/5LQLAXJvZsgC+aj9UWGgVAIFSEKL00Sq3mkps7\nR+P7zv0wfPhwZRqKwoULy7M5NWFBPrh8+bLZKTJzAdDXxwc7l4wTX/vKC8PmwTr1W2Nc1zrK8jRR\nrikXk8ML+yN/vZ5yZJAlV0VZ6dk3AOoZfubhGLtWC2wv3M+LoH3n+mWxZU2JeYI+AKrW9miIDn9p\nl9Hxvr0G2YvWFLV6vT/tdzUcjWqWQoN288RyvTvXrpj9Xd1//Ew+Qy/uAdCY+v3EuAHQ7z7+Pa6d\nVu51fDVylewramN7Z/bGXR/Tz8IASESOigGQEkVcdgF3qPQ+ug/6HT5y/ODgQuRvO0HUN7cMR5Xm\n40QdUwBMrdSByvzlxdVKEHhfW6h6fQ3+xgkwHmLaAri421d4LDfffZDC9J+TLkVRWcWB9xWsOWca\nZn6pXlhWxhwjACLoqfgd9Bw4WS4A8iTTRez2Vi+yrf+RjWz1MSZtuCbq/3r8gC7D9os6wH0t3vlU\nC4DZlefPP+ghatX8xZtlFV/RB8Ae41fLUVS1vv5WVrELvbAJ75f+XY4UXg+waM9NOQAOrDdc6JsB\nkIgcFQMgJYq4HgPo5afGN4Nn99zgLTelXb/7RCvMOHvllpjfuvlAzPUeuF3B/ee+cpQwMQXAuHjk\n7oaRbQy7ii3jIAFQ4ecb9VqFZ85oWwPfPrkv5uY8vnMVvuFAiN+LSBdeDlM+/qysE8p8AIyPWw8e\nYvAC7ZqMlmIAJCJHxQBIicKaJ4EktlMHN2PxokVYpEz2tHKp9hpWbpIXsjbD0U4CSXyh+PffZeLn\ndPjsQ7ks7sJC47PjPno+T26I17B02b94FcOnZAAkInthAKRE4cwB0JkwADoXBkAishcGQEoUmTNn\nlhXZ0v37960SAMuVKydHZEsMgERkLwyARERERC6GAZCIiIjIxTAAEhEREbkYBkAiIiIiF8MASERE\nRORiGACJiIiIXAwDIBEREZGLYQAkIiIicjEMgEREREQuhgGQiIiIyMUwABIRERG5GAZAIiIiIhfD\nAEhERETkYhgAiYiIiFwMAyARERGRi2EAJCIiInIxDIBERERELoYBkIiIiMjFMAASERERuRgGQCIi\nIiIXwwBIRERE5GIYAImIiIhcDAMgERERkYthACQiIiJyMQyARERERC6GAZCIiIjIxTAAEhEREbkY\nBkAiIiIiF8MASERERORiGACJiIiIXAwDIBEREZGLYQAkIiIicjEMgEREREQuhgGQiIiIyMUwABIR\nERG5GAZAIiIiIhfDAEhERETkYhgAiYiIiFwMAyARERGRi2EAJCIiInIxDIBERERELoYBkIiIiMjF\nMAASERERuRgGQCIiIiIXwwBIRERE5GIYAImIiIhcDAMgERERkYthACQiIiJyMQyARERERC6GAZCI\niIjIxTAAEhEREbkYBkAiIiIiF8MASERERORiGACJiIiIXAwDIBEREZGLYQAkIiIicjEMgEREREQu\nhgGQiIiIyMUwABIRERG5GAZAIiIiIhfDAEhERETkYhgAiYiIiFwMAyARERGRi2EAJCIiInIxDIBE\nRERELoYBkIiIiMjFMAASERERuRgGQCIiIiIXwwBIFsmdOzd0Oh0nB5yqV68uf0tERESmGADJImrQ\nIMdUtGhRWREREZniuzdZhAHQMQUEBODTTz+VIyIiIlN89yaLMAA6JgZAIiKKCd+9ySIMgI6JAZCI\niGLCd2+yCAOgY2IAJCKimPDdmyziCAEwKNAXjx49whufALkkqpfPPMVzgoKC5ZKkjQGQiIhiwgBI\nFrFlAHS7dApFcyYXX2Pcsq1iWXjQGwzvVBOpchTD5TseypIA6FKXFo/pvbpzCCPnrcOyCf1x55Vp\n4LPl6z28fATW7j6C3zs2R0wxU30N+slWGACJiCgmDIBkEVuGGL1Pc6bGF23HyhFQqsQXslIpATDz\n57JWhSqvKZOso76+uL7eY9v/Rd1Wg+UodmFvbyFb2S5ypHydNB/LytTiv9rIyrYYAImIKCYMgGSR\nxAiAgJ/4OmdfAL3qlJXL9EwD4KnlA5G3xSA5AloWyoKVZ17KUeyvd+ao31CvcUf4y/HevXvNTvc8\nfeUzNH/9UBR/L9kvR0AK5es8C5WDCGo41aFspZp48FL/FWyDAZCIiGLCAEgWSZwACJxfP1J8rTdy\nbGAaAAfX/RytB82SIyWYNf4YLcb9J0fmX29YoA9+bt0IA0YYPi6+Ps6lw+L9T+UIKJVGh43nDcFT\nCA9DsL83Zo38TbyO5UcfyAesjwGQiIhiwgBIFkmsAOj74ALy58qI8s3+lkv0TANg99ol0HX0ZjlS\nA2ERNB6/Ro7Mv94bp3ehWvWvsOfkTbnE4OHDh2Ynbz/To/wKZ9Vh/VVDPP0ktQ6bzj2Xo6jCva8p\nryWLHFkfAyAREcWEAZAskjgBMATfdhsl5urXO/rIT1ssmAbALf+0Rq3e4+QIaFo4GxYeUE8W0cT0\neoN8n6Hxd9UxZvoKuQTw9PQ0O/n4h8hnaH76PCdmbbklR0BK5evcj/6kZCFHctv97BgAiYgoJgyA\nZJHECIBVytaQFXBr21Tla6aXI1Wkk0DC3kCXpqQcAKkjvb64vt5BXVuhXf+47xJ+67YNRZsbTlQx\nfY3m1W0beWum9TAAEhFRTBgAySK2DIA+LzxQMIMOp54YtvgFhyiBT/mayXOWhW+QuhUuUgBUbJ74\nM/a5PcaR/yZgxSF3uVRjy9fbvW5pPH4bhEm9fsBtnzCxLPCVG6p/+6OoZ3X5Chnzl8Gyf/9Fv99H\niGW2wgBIREQxYQAkiyTGFkBj4eHhstLXUQNgTGz/erXgZ+z42RuySjwMgEREFBMGQLJIYgfAqJQA\nmKWCrGNn/9ebOBgAiYgoJgyAZBH7B6pQTJ4yBTNmzMD+i3flsqg2r14gnjN58nS5JGljACQiopgw\nAJJFXGWLmrNhACQiopjw3ZsswgDomBgAiYgoJnz3JoswADomBkAiIooJ373JIokVAEOenFe+1vty\nFL3MyutxeyUHCRXmhU2bdslBzA4dOC4r9bI1D3DgwAHDtH+vfEQVHLH8xAU3uUzz6sFNGM5ttg4G\nQCIiigkDIFkkqW0BfH5tK3rMPSzqgtlyinl0ds34BboU/5MjoOL7KVCsWDE5lUCmT7+XjwB/NasQ\n8diR+75yaRju3rttk58hAyAREcWEAZAsktQCoPH3c3rVQLQes1GOIvG+jcWb9yjPLyKG4eG+eBMq\nSiHQYyfmHX8iRz7oP/+QrKNiACQiosTGAEgWsXZ4Cfd+gG/a/4G7F4+iTM0WmDx5InyCQ/BT/crQ\npVW3toXhl6Y1oUuTE6tHdxNfv3zLP8TH7lwzG8mVsYfpbXpxa/dC5M6d2+xkIvixyffjd30XdJkN\nW/iM1azbDQi5HxEAI2tV4QNZAZO7fCM+rzot2ntFLjWw9s9QxQBIREQxYQAki1g7vBRJqYOnvJlG\n79ofY/xWeSu319ehy1JGq8M8lK+bTqsVxq8hl1I/NtoSFx/+7geVz5VLjoDQJyeUcRY5MhjVRe7a\nDXBXHjcfAN8t3VBWBq9unxSv9ezTALlEwwBIRESJjQGQLGLt8PJJeh3Oy5M4Jnz/GRaefqYNvNwM\nATDoAXQp39NqhfFrKGImAN7ZtxRFihQxO5l4dVn5XKnlAPC7thO69O/Kkebl1T24/FwOwh4pz/9I\nDgxenFuBtTe85cjU/X1zUKvDDDnSMAASEVFiYwAki1g/vPghe65PsH7Df2jw0+9ymcKCABgfxp/r\n6s4Z+F/TwXKkWTW6Ez7//HNtKldSeX5aURv7vqRh928UIR5oN+WgHGgYAImIKLExAJJFrB1eOtUo\nLKtIvG9Cl/UzrVZClC6V4ZIwxq+hoFLrN9AlxPjWpeEmT9JtXyYbnkc6ntBE4D3la0fdBVyoZjdZ\nac6dPi8rYHi3H2VlwABIRESJjQGQLGLt8LJvwUBkypgRGeWk06URy7s2r4P8+fJj1YGr6N2uEfLn\nz48pK/Zh3OBeou4/diEOrJiLfErdqGM/8TEJNWnoIIwcPQJXHhmO1UupfJ/LjtyXIynQQ/naX8qB\nxvP0ahzzNE2NY3/riJy5PsDwf+bIJQYjhg8Wr7/Lr4MhD320CgZAIiKKCQMgWcTaAbDpL7NlJb08\nAY8gWVOcMQASEVFMGADJIlYNgMHPxOdr2rEnxo37B7/+3B69Jv4nH6T4YAAkIqKYMACSRWxx/Nrz\nRx64eeuuHFFCMAASEVFMGADJIrYIgGQ5BkAiIooJ373JIgyAjokBkIiIYsJ3b7KIswXAZ488ZZW0\nMQASEVFMGADJIs4TAEMwf9Qv0KUuJceJY+qwX9Gp56+466VdGmbviinImDo5Kn3dGMbXq350fifS\nKj/LEtXkbeYsxABIREQxYQAkizjVFsC3V6FLJe8mkgjUQCevKS08u7gSv01bjhcvXqDrt+WUn512\nn+Fgr0dYd+y2qCd2/wYfVe8sakswABIRUUwYAMki1gqA98/vx+x/N+DlPcMu2nOHNmPK7CVypDl3\ncJuYn9i7AdceavfbfXr3EjbtOSVqVeDbp7j1IgCPb5zDhj3H5FLFmytRAuCSOZNx+PIdOVIFYeLE\nifAPDoK/BbeUK5RRh6MPA+VIc/ig4Y4gKnM/u+D7O9D6nx1ylHAMgEREFBMGQLKINQLg3f2zsOq8\ndgO3DzOXEPOeNQtiy3Ul4Pndj/ga1y+eFvXM8ZNx7PhBUS9eOA4HDx9FyVwpMX27G148uIPS7+nQ\n7pfe2HfkKL4onAXFGv0hPt40AIaiQKkGour9XQnU+HmqUoUjd8k2YtnsPj/gtagMLp84gv3790eZ\nzlxyk8/QvLmxFbpkOeFx6yJWrlgpl0YWgoz/+0HWBnVrVpeVZRgAiYgoJgyAZBFrBMAdE5qhWf9p\nog547SXmo7o3UyISEOb/2uRrGNf1C+twWm4w3DpnAMoPXiDqn2rlx6qLL0WtivgYowC4eMB3mL9p\nN7Zv347N80bL5wRBlzE/gsUzgMDQcFnFz6z236BEA8NuXPVze0f6VENbVIj4OppQrFk6B+9mTI4U\n75aTyxKOAZCIiGJi+bs3uTTjQGaJjj9UEp9r5jrDLttW7X6Gf3Coydcwrr8rosPhe9r9ejfN6IdK\nQxaKuv1X+bD01GNRqyI+xigA9v4iEx6KytTJjVPF88vX7SSXGGxdMhdTp06NMq3avEc+QzOsZTW0\n6L9CjoDRTUrht6lb5QjwPL8Z+269laOokitfP2HR04ABkIiIYsIASBYxDmQJFRaiP9jOF6mVz6fe\n+led6yUkAC4/+0TUKnMBcGK3ivj6Z22ro+r1oytAuCF2da9bBl0mbZaj+Hl6bgXer9VdjoClv5fD\nkuNPRR346AKWH70natX9+49kZZA9ueU/UwZAIiKKieXvNOTSrBEAN01riAdyg1iFrNrnUz/vpvP3\nsHLOKFFfuHA0YrletZw6bL+pBcB147ugRIdJou7wTUFUbvW3qO/tn4OWYzaJGi/OKh+fU6uDn4vP\nVal+G4wY8Tca/jZTWfgKXcdtEQ/f2T4a4zdfE3VC1C2aDQ/91SoUH5asIZa9eXBOfM0smTMjszJl\nSp8abt7A9e1jUKtZV7x59RLzh/fGkTvayS2WYAAkIqKYMACSRYwDWULdv3oYO1fOQYMfmsNL3fyn\n8H18CR1/6S/qCYN64o2y/OK5s3B3d8eZMxfx4M513FHqa5cvwNfvJa663cKdW2547hMitgCuOXsf\nLRo1wPoDF8XnUJ27eFn7eDd51m+oL/p26YRVO05q4yAfXL58Hs0bNMCavYaPS6jHd6/D7a7hrOY7\nV93E1zee9B4+uIc796JuDUwoBkAiIooJAyBZxBoB0Np++roA/tWfHeKiGACJiCgmDIBkEUcLgLfO\nHEU25TWVa9ADb/wtPZXCeTEAEhFRTBgAySKOuAWQGACJiChmfPcmizAAOiYGQCIiignfvckiDICO\niQGQiIhiwndvsog1A+Dh7atRtczXcmR7+9Ysw6zZszF79ny5RBPdkYPPn2u3q7NUuNH1BuMi8vM3\nrFBf82zMnKNd99AcBkAiIooJAyBZxLpbAN8ony+XrG2vSaHMMNwwDjj07yh8VetrZEmuQ/GGA+RS\nwOfeEfSatFbUc39vgvOe4gJ/imDs2rE5zj+DgGeXxXP10wNv9WZ3pjIqy8/7yEHwC5PnH3M3vXuI\nuiw6DIBERBQTBkCySEwhJN7C1YszJ2IALJwFDwPlAKG44KHdh1hl/H3Vya9DxKWZldeYsVQPOdDE\n9WfQokVXWQFzfm2ArJ/WliNNrx/r4uN3kuOiDIAdWrXUCsWRhf2gS1tMjjQ6XTJZRcUASEREMWEA\nJIvEFH5GDvwZPzRsiH5jZ4vx+iWTUL9Rc1EvHtsbJT/+GNneK4Sn+hBmFAAH9+uBhg1bi3r2yMFK\n3VDcIk7ltmcpalYsg/TZP8Aj/zC5VPP6+gFUq1bN7BSZaQA0FoamAxbLGtgxuQvS59DCVO86/8M9\nX/2t6zQJCcGhjw6hYPXf5Ah4eGwFbvsCedLpIgKgibAnSFHwKznQMAASEVFCMQCSRWILP7lS6HBP\nH7IC7uHU03ARZnS6fGLRidWDUbLLKFFH3gJo/LlTKLXY8ep9B23/2SWW4cUp5TlptToBzAXAk3s3\nIL3ytboNMz0ucOZvDcXrMXcUYEIC4OjmpfEqWA4Qgk5jV4sqbzQBcMf41jj3NOIDBAZAIiJKKAZA\nskhs4efapjH4sNavoh72S1sx17t2ah+G9WqFwp3NBcAwk4CTUgbAFUProcvQ8Zg4caIyTcCwYcO0\nJ0he986iS5cuZqfIotsC6PPMTXxfBz20+wwj7BW++PFPuO2eL5Zfeq4/BlAT7wAY8Ag9Zu6VA6Bt\ny/ayAvKn1+GBrA2CUb+Hdp9jYwyARESUUAyAZJG4hB/9c4bNPyzmqk/z5BHzU+smxCsAzv+jDCZs\nuaUtlBJ6v4/odwEDywd9hel7PURdNqMu4ms8OrECuqz/kyNNfANg856jZaV6KT4+8rT+muF4xAZN\nu8nKFAMgERElFAMgWUQNK7GZ0q0KypWtKEfAzW3DUaren6I+vWYUinQbK2otDL0v63CTgJNM+Tpq\nVntycpWyPIsSDzXH186SVfzFFABbVng/IvRVyaqL+HqqVPm/kJUmLj8DvUZtDGcX3zu7Fa9N9+oi\nXzodbhgdYtiyoWGr6ev7Z3H9peEFMwASEVFCMQCSReIWfryQr1ZfWQNhPu7i44qXrYQtSycpdRrs\nO3sPs0f2EMtX7z4knlciWzLkKlgUfSZuQGpledfB08TytrWKieep07qTj8SyhDANgIFIq3y+XB8W\nR7sf2+O1/owTIQxVv66HdevWYfiAnnKZZsN/S8XrGDX734iQ+GEKHYrVMz1TWP0c6nGM+tctpoym\nZ/Wq1PsYn5enHKvHA5o8X5dOe0BiACQiooRiACSLqMHEWcW0BdBSQzrXlZXtMAASEVFCMQCSRZw5\nAH6fJzVeydqadq5bhisPDMfw2UpMP3sGQCIiigkDIFnEmQNgcGAgHj54gAcPtJM9nMXTx+prfgA/\nf3mWshkMgEREFBMGQLKIMwfApIwBkIiIYsJ3b7IIA6BjYgAkIqKY8N2bLMIA6JgYAImIKCZ89yaL\npEiRQlweZdWqVZwiTatXrza7PDGmZcuWoWTJkvK3REREZIoBkCwyduxYDB061C7T8OHDkT17dvTu\n3dvs4/ae1K2jo0ePNvtYYkwLFiyQvyUiIiJTDIDktLy8vJAtWzY5cjzNmjXD+vXr5YiIiMhxMACS\n00qdOrU41s2R8RhJIiJyRHx3Iqekbllr3LixHDmuXbt2oW5d298VhIiIKD4YAMkpOdOWtQwZMsDb\nW97gl4iIyAEwAJLTadSoEdauXStHjk/dTZ0yZUo5sq6bVy/KioiIKO4YAMmp+Pn52SxM2ZJ6Qoh6\nuZzoeD+5gS4NKoktm4UKFRJTvvffQY4Wg+Qzotqzdpry/BRyREREFHcMgORUsmTJglevXsmRc4l1\nt3XAvSjP6dLzD1mZ80x5vvOFYSIisj8GQHIa+/fvR9WqVeXI+ezcuRP16tWTIzN875gGwLBwWWhG\nDuqLGUs3ypHKNADePLULA4aMwb171+QSRZg3BvTujeNXPOQCIiIiBkByIs504kd0MmXKFP0JITIA\nhoaGiqlexz/lA4bvfeuErvi47l+iNg6Aby6vx9RDWsj7stzHYh784jxajVgp6q/yZ0C/pYdETURE\nxABITqFz586YMWOGHDkvf39/pEqVSo4ikQHw2LFjYqre9m/5gPL9d9GOBTy5egxyFm0pauMAeGvb\nFBSv017U4WFBYv5DwQxYtXI5li9fjj9/rgtdujxiOREREQMgObzw8PCILWBJQdOmTc2fEBJpF7DP\nCy9ZAdcP/ItpS7Yg4PYWZP9fK7nUdBfw1N+7io/v9NdcMU6ThH5mRERkXXyHIIf3wQcfwN3dXY6S\nBrOB1veu2eVhb29BV6i+qH3cNikBsLWojQNgYECgmKsKZ0sGN2+g4YdpMHjZCbkUuLjX+PhBIiJy\nZQyAFCedO7QXd95o0qwlunXtLOrGTZphwsLoL21iDefOnUPRokXlKOlQ7xBSv74W6vS8rm4yGwBD\nXlwUy++530G3FjWRNk8ZrNy4U3nAPeL5N/4dja2XH4m6fpH08AkD/B6dEo9Xr98S7ZvXw5JDd8Tj\nREREDIAUBwH4Z/NlrXy8F7rM2kkGqiYdO8vKNswFoqTC+ISQYH8vPHryXIwf3Iu6tfPo5mVYs+uc\nqMdNmifm9x48Es93f+CJVw/u49b5Q+jevTveKuEvQsAr/P3333Dz9JELiIiIGAApLsKCECLLt1fX\nQ5epsBwBL95qwSIszDh1xCw4OFhWMRswYAAGDx4sR0mPr68v0qVLJ0dERESJhwGQ4iVyAHzpfhmf\nZNahcvcBqFntM5RsPQjfFc2OOl3mKI8GYVKfBtDl+FR7cvhbfFqtKU4d2yO27HkGmF7nLrKkvPVP\nT92Vvn79ejkiIiJKHAyAFC+RA6Bq8ciOqDPacG/efdN/QqPei7XB09PQ5f1MlF/nzSDmqsk9KyPP\n5z/JUVSffPIJLly4IEdJmysEXSIicix856F4MRcAFw7rgLqTNsgRsHdq+4gAGP7kZEQAVINOQECA\nmGLa9nfnzh1x5q+r2LJlC7777js5IiIisj0GQIqXOAXAKe3RuPcSbfDMsAUwixIAnxgdKvji0S1Z\nmVKDYkiI/qhD15A+ffro7xBCRERkZQyAFC++lzdAl/5DOdIsH/MT6o/bIkfAxbWD8NG3P4t66age\nSFmggqiXDPpBCXdZcfHOEzy8cRJjVh4Vy41NmjQJnTp1kiPXoZ4QkiZNGjkiIiKyLQZAirPrp3aj\nd49u6NWzB0aMGieWBTy/gZ7df8bPP/+Mw5cMly8p91FOfFLheyXZXMJX9Vvg+oNXYvn4/h3EFr5u\nQ8zf1s2Vj4dTrwu4dq3hWEoiIiJbYQAkh1GpUiXs3btXjlyTcQCeOXMm2rRpI0dERETWwwBIDuHZ\ns2fImDGjHLku9ZIwderUEbuD1TCYLVs2+QgREZH1MACSQ0iZMiX8/PzkyHXVqFFDBD/jiYiIyNr4\n7kJ2t2LFCjRo0ECOXNP06dOjBD/9FB4e8wWziYiI4osBkOxODTkEZM+ePUr4UyfeKYSIiKyN77xk\nV+qZr6tWrZIjMrcLuGXLlvJRIiIi62AAJLtRj/lLlSqVHJHe6NGjTQJg2rRp5SNERETWwQBIiWbz\n5s1wdzdcKzBTpkzi7F+K6uTJkyYhkIiIyJr4zkKJplGjRiLMlCtXDjt37hTX/aPoBQUFRQRA9f7J\nRERE1sIASIlGf207/aTu6nRm6tnLX3zxhQiytppq1aolflbFihUz+7irTF999ZX8qRMRkTUwAFKi\nMQ5/xtOJEyfkM5zL8OHDsW7dOgQHB9t8+ueff8wud5VJXU+IiMh62FUpUfj6+kYJfvrpyJEj8lnO\nRQ2AmzZtkiOyJXU9ISIi62FXpUShBqXIwU+dnPnuHwyAiUddV4iIyHrYVSlRNGzY0CT4ZciQQT7i\nvBgAEw8DIBGRdbGrUqJQL/miD3/ly5eXS50bA2DiYQAkIrIudlVKFPrw9+uvv8olzo8BMPEwABIR\nWRe7qo0tX75cXC7Eladly5aJN/D27duL276Ze05iTytXrsTbt2/lbylhrBEAH14/gqrFciN5ihQo\nVq0ZnniHieXjhvQUP7NhUxaJsVo/ffoUz16+FmNVj56/YfrEMTh110eMg/y9xXOKJdPBsu8seue2\nzRGvpXzN7+US874vZriv8bNAudAC6uchIiLrYVe1MfVYtzlz5mD27NkuO7Vo0QIzZsww+5i9JnU3\n9JUrV+RvKWGsuQWwWZm8+KB0fTkC/hvVDk+D5UAROQBVy51GVkCVd3Twl7Xqy5w6eMk6ZmHo3Ka1\nrGPn/eg8Trhr0XJAszL4sssEUUcW7nUVa88/FAH77VtvudQyDIBERNbFrmpjuXPnlhU5EnVrpCMF\nQFUKJeTM2HMb3je2Y+Hh+3KpxiQAhT1XxhnlANgxsQ++6vuvHMUeAINf3cf3X1bC5H+3i3FoaAhC\nQqJOoaHa1khz7h6YjQlbrsuRqfrFs6Fpux54om2YtAoGQCIi62JXtTEGQMfkiAHQ684BEXR6T90g\nlxgYB6BHB5ci2bsfyhFwZ+dkpCn6pRxFHwCvHt+JyjW+wombD+QSzc2r58TFuCNP5y+bhlBjNas3\nkFVUZ8+exq8dm4jXPGThAbnUMgyARETWxa5qYwyAjskRA6DqvXQ6ZClaS44MjAPQuQ1TkCVfPTkC\n3PdMQfJCVeTIfAB8cn0HylWohBv3X8glCRTmjzXLZuOdtDpkK1JXLoxG+BvxusPl0BIMgERE1sWu\namMMgI7JEQPgj1+XFvP0StiZtNVN1HrGASjgzm7osuSXI+D49EH4qGZ3OYp5F/Arj0uo/ll5LFh3\nSC4B9mxejnnz5kWZVq6L+RZ9cQll/b7OjhdWSIAMgERE1sWuamMMgI7J0QLg5n864bUMSgGPT4jA\nE6oNhcgByHg8rFURbLr6Uo7iehJIMLq1aYCOA8fKcfzpdOllFb2OXxaTlWUYAImIrItd1cYSKwA2\nKZQSf/97To6i8faaVd5I1yyehTXbDFuQouX/BEuOGW/JCsOYEUOwZmfUe/9OGvkXVm85KEcGOzas\nwbjxM+TIehwpAO5Y/Dc6zzguR5ryH2aALk1eOYoagNy2T8LU3beUKhAFSpkejxe3ABh/26d3x7dt\neuHpE08M+aUp7htda2bM38PFrl7vmzuRr0wteCrP+XfaKNx9a40dwAyARETWxq5qY0ltC2DedClk\n5Y2i3/4ua/OyJNOhyUztTFOEeUOXIT9++aUHsibXIW1e/d1A/KBLm1NZ/gveSalDspwl5XLgw1zv\nyMr6AcBRAuDjOzfwwOMh3G/fQLDMSq+f3ofHo8d4/PgxrsnXaPb7D3wLt3tP5cDAVgFQ9dTzMTyf\nvZIjvXAM6v2bEu813q+f4dFTwxZJa2AAJCKyLnZVG7NFAAwLDpBV4vI8thhFahpC3/vKm7JXNFcK\nGdzyGyyf0B2NZADcOGu4mOvp39C3Lhkn5nrq8hBl7ndnJz749i9toWLhH03QcPBaObKco54EEp34\nBCBbBkB7YQAkIrIudlUbs3YA/OW7YnjwOggnVo9B+So1MXTYeNy7tF+8Qc7a9hD3L+5HZqUesHYP\nvqhaUSzf46bePSIEA9p+p4z1W/AMWn32GT4zMw2Ys1k+QzP4+w/RavJGOQKalk6GCftvy5HBi0sb\ncfpJCGYOah0RACPT6TLIypROl1rMH+ydj6yFm4pa5bZ1JLJ9brhQsqWcLQDW/boWhg4dijEzF8sl\nUd0+u108p/G3teAnlyUVDIBERNbFrmpjVg2A4S+VN0LD9d+M3xR7lM2N+TsfibpXk/Los+qkqA8v\n+B1lu00TNQLdlY8xXEA4vuoXfAej1xiOVWtbMxW6zYx63F6b3xeIeXQB0GP/TGwwOmlB7/nppVh2\nylOOQk2+v2UDW6LUt7/JkeWcLQC6OgZAIiLrYle1MetuAQxW3giTydr0TXFAubyGANi4PP7cdU3U\nJxf9hc+6Thc1Qu6bDYB18udHfjNT14lr5DM0A6rnQa95u+UI+L6wDuP2mW4BbFm7Gh553Ie7uzuG\n/9wA3wxfBM+Xxjskw/D9r5Nlbap25zGyMpg1dSoOnL6Kku+kxLrLz+RSyzEAOhcGQCIi62JXtTFr\n7wK+s3c6WnTthyF//IpHRvv5LAmAcXVzx1SUbGo4Zi+b8qb82vhaJYqRI0di2LBhYqpXtSSK1muD\nRdvPyEeBRq16yUoRbjhDtEEzw3Xs1JMKjL2+vg1p8n4uR9bBAOhcGACJiKyLXdXGrBsAA5GpYCMl\nN4UjPCwUQUHBcrkS+sp9gGUHtLs89GpcEsMP3hX1xRWjUK6DDIBhHsobaSatTqDUuuSyCkKeCh1k\nDQSHRL3cR+RdwJ8oP4vpUydh/PjxGD96MGbL4wf/V+B9zJg2WVs+5m+M23xRLFcFPrmKDHm0CyRb\nk6MGwHz5DJd+ic78vs0wbdMlOUq4R4+0PxhiFWpYz/SCgoLE5O/rL5eYirw2qM+1BAMgEZF1sava\nmLW3ADb/+ks0btxYTI0a1scHn7XAw3P70Kpla/zY6We43ziBH1u2QtufusD93m10atsSrdq0wcV7\nvujX+2e0adMaS9YbduMmxMRRo7H0P6PP4asGy6gnl+z9bxam7tWCyqi+LZSv3cZkUv0zsIPZ5Qh6\niT/++BtHL7prYytz1AD44P49WUUvPMhLnCmdcMEoXEm7lVyFokXEPFqh2u3cjHkcmS+WqdP3/RfJ\npQbJlOVucq9/iNetiOeq07nHvtoD8aR+LBERWQ+7qo1ZMwAe/3cwfGSt93O7jrKi+HDlXcDN/5cO\n12RA87q0AWXaTdAGZjRo/nOU8FWv3UBZRdWjcQN8mUqHG/Lzt2reSSsUmyd1gS6L4Z7F8cEASERk\nXeyqNmbNAHhlz1zxRlihUhV88UUVfFq0HKLunKO4sHcA7Nz0B6xevRo1fuiAo4cO4M4TP6xfMBYf\nZE8rHt++bAoKvpcRwV6eSKf8zovVbCWWv/a8gdpVP8efcwy7yTVvULFiRbPTm0j7Y9V1yLDouTJO\nJWtTs/o2F3Pj8HXnwGwx1ulSYv2JO3Kp5t6hebgXCJRTHtcHQBNvryJtOcNhA/HBAEhEZF3sqjZm\n7V3AZB32DICbxrTH7+u0AHdu7XCU/WmiqFXGQUetj9/R7rqhhsDHMu0PqvMpRiy+qg3izStSmAow\nG64CPU9j5xX1+pHmw9f109vF8o2Xn8slQfh5wjZRRRcA5/asifvmDxmMlbnXQERECceuamMMgI7J\nngFwVOeq6LVEu57im9MbkfOb/qJWGQcd4zpnch3cZHiaXLesmQDoHeVYSv3kbbIFMDBSmIocCDWd\nBs6TVfThy//hfqQs3lDUbZsZtuxVUJ6vj4URwl6iwzjTywrFR3SvgYiIEoZd1cYYAB2TfXcBByCj\nLgMeeHigfoUiysjAOOgY18YBcJLZABh36ueNyIS+l6FLYbi4uMrz+ALxnMiTuRsQZi/ZSPn/C3Hi\nR+Tn77jurT1J0aTjn7JKGPXzERGR9bCr2pgzBcBAr6e49eiNHCVt9gyAry9twElDNjJhHHSMazUA\n3pQJbEq9chi15Lo2SIBVQ+pjwxXtBZxZ0h/T9twSdXSiC1+Pz67C9TdRbwb9ufL8e0ZXfanXsous\nAI9Lu/DQO9LFI+OAAZCIyLrYVW3MWQLga08PVMurw/KjtrnsSnTU+xbrvTy3UrzRG0/6qHD80HZ8\nUvB9bHKLfB50wtgzAJ7bYbiMijalgV84sH7JVDGeuuog9m2eI+pxc//DpeMbRd2q3z/weXoXudPq\nkL9iI/hFzV5x9suPTbB69Sr0GW24jMvAph+jQvMhcmSgfm29Pk0rK+O0aNulB+48M39Jl2LK86+/\n1Wr196t9j/opYf8ejF8DERFZjl3VxpxpC2D3upkTNQCO++UHkzf2bp1+kpXm3byVZaVpVCAddt0x\nuv2JBewZAMf06Skrg0GLDsmKzGEAJCKyLnZVG7NOAAxG0+++xqINe3Du6n25yButf/gWPQaN1caK\nuzeuYcDvixHm9xJNGzfBswBtE1Hfbq2x4bC2m8//9TPMHqNt5enRvjlW7jDcpi1yADy9fTlqf1MH\nVzwMp3T+N3s4GrXtif17IwefMJw7d87sFGhmS9XzK5vg9jo02jf2kEcHMWar6WVGvs+XNAKg+j0P\nHDMdbm5uuHj2JKpVqyEfoegwABIRWRe7qo1ZIwDmTJdDK0JfYMzKC0oRHvGGeHpxX3z0dXclf4Xg\n9JrRyPp+Tew+egn3Tq9TnpMV46YuQnCwdqanegiZj5+fqIdNmqMs90F2pZ4nb8lmHABn9agND5m1\nkivPufg8EJdWDcUpuWuv0ZdltMJIYGCg2SnSZeiEVgPniHl0b+w9qxeQlUFSCYCq0/u2Y8KECbhy\n21MuoZgwABIRWRe7qo1ZIwCq14BbtF27bEhYmHZU3JQp88V827zf8W7FxqJ+feI/ZP9fD1GrjN80\nS2TQ4ZY8ZMt4+aPDi5A6d21RGwdAXapsmDx5spiqfJIVLX/bgPOrhqFwFfWsT3PC8ebNG7NTWKQE\n2LLBd7KK/o09R8FasjJISgGQ4ocBkIjIuthVbcw6u4BDUa1EfvEmePuFdnrl7tl/YuW+cwi4tRk5\nKmih7NXxNcj2cTdRq4zfNIulVz5WZieTN1Ovs9Bl1La2mQTA5DnFPLLlUweLj2/UY5xcoheOGzdu\nmJ2CjHYBv72xHWkyZkPGjBnFpH4udS43LAp+Nzdj8cmncmTAAOi6TNZZIiKyGLuqjVkjAPoEawnK\n6/ZB6LIXVVLUBaTMpd2my/fWFuSUAVDdAhjfABhwZw8KNx8tajUArjh2T9Rit+8Lw6a71esO4eUb\n7c4QqoKZdXgYIgcWMPfG3uazPLIypQbA3XcTeCuJSBgAnQsDIBGRdbGr2pg1AmD67B9qx+89Ooty\nrZWw5nVNvCHu2b0TXX9qDN17pbBk2U4cnt0XukyVxMeEBb4Wz/GV11HJptTrL2n3Z1Av2jt500lR\nf5RBh0BRAdXz6tB5ymZRH1qsbemr9OWXyPdOdqjbHfdN/gWrD1wTjxdIY3w/2YQz98b+fmkt3EZW\nMqMO4+Ut1CyVVAPg0qnDEzUsHT+wF21/GSxHBnsX/I3q1auL6dBt/fbdMPTr0hr1WnfBa6PrBMZF\nYn5PRESugF3VxqwRAN8GhWHHf/9i1zFDYLl/+Rgu3tZ2k27ceVDM9dfMCwoOgX+A9g4b6O+P4EDt\nCsLhodoy8WYa+gqzZ2vHEapCQ/TvyOERx+z5v36MNf9t0AaKFy9e4tVDN+XjFsgltmA+VgYp34cQ\nHorQyAcVJkBS3gKYWGEpXD0e9dU55Q8Q08v1qGq3GoA1a9aISS9TusxYvXo1ev/4tXiNXvH4NTIA\nEhFZF7uqjVnnGEDr4pup/QLg7bvaLnbj7HP7tunlbtQtZYEizYfihbdhU5nHI9Mzhl+81u7a4vHQ\ndHnk36/v6yd4/MJwKR+Vh7t25neQfzw3xUX24kKUALh1fBtMWbRWjjRv3I7JSlMndxqsORX3u85w\nnSUisi52VRtztAA4aeRgZM6cGX3/+kcucU2JHwCDUKB8c7x8+QL/y5FGLHl2bTfyVmmHl8/vioCj\n7oo/s3eLqDds3YRf+/wm6m3nTqFxu59R5X/voXa3aQh8/RCNK+dF5dbd0ah1Z9Qo+QGyFtLO5FYZ\nh6W21Yvj0oOnmNStHvJVaSaWfVv8Qzx88RJrJvTA5E0PxDK96/v+Q/fu3c1MUS9eLZgJgH8P+h0V\nP/1QvI5x6wzXmTT2cWrDXV7iwvh7IiIiy7Gr2pgjbgGkxA+AoY8PoXj9/trA3wvq+TN75nXHfHkf\nXvX+uVdfiRIFM+lwS973d9fY1mjad5U28L0BXc7iory8eTRKtTLctk29VNAdX227oj4shb25hgbD\n1uDZs2d4/vKVWB5s9LjqxvWoZ1vHi5kAGMH3vvhaUW4YF/gYLUdulIO4YQAkIrIudlUbYwB0TPbY\nBdy5XikRZGavN+wO3b5kPNbuPY3KyXW4Jk+yLqQEwBveWr1jQis0779cG7xxg+7dEqK8uHEkyrf/\nW9SqRh9lwfor2i5VfVhy2zQcfdecF7WxK3sXiufUbmk4Y1zv9H/TUaNGDTPT1/IZkcQUABXL+n+N\nTddN799crXYrWcUdAyARkXWxq9oYA6BjsttJICFv8G6GZNh05RWmd66IrbfUbXJAJSXgxDcAVvhp\nqKhV3+ZNh+uRtgB6Hp2JrB/VFbXeG1/DtXv++ukbvF++jRwlUCwBcMOIdjCOfy1/MLqQeJj2vccF\nAyARkXWxq9qYtQKgz8uHaFjrSwxfvF8usb2GDRth2LBhmLt+r1wC3Lp2Ac+NQoTezasX8OS13G9p\n5OGNS9h3+IQcxc3DO9qJEuZcunBJVprwgNe4cNlNjhThvuI1D+zdBbOPartXzUnsABj0eD/Oyr2t\nl5b2wvLzb1EyVwoMXX0eoW8filvyXX8TKi73ky+NDvfkAXL7J/2IFr+v0AZB96DLrO0CVgNgukKG\nu6XokhmunWgcltS6WvM+uHDhAr6vUV0sa91/hJirUuWPZsteXPnfgi7tZ3KgCH2Fhh0GiPKFxxWM\n/XefqFXfFX8XLVs0V9arhmKq2m6kfCR2DIBERNbFrmpj1twCOPWXohi98ogc2Z5Ol0pWmiplP8eM\nmbPFm/G4defkUu3N+YM8ecX805qG3Yo/lCgYcY3BL/Jni/W6gUFvn+HSweXK58knl5hy2z5Feczw\n8zy46E/8NX4y2nxTGrqMH8qlmnNLf0PHldq1Ds1J9AD48jpG/z0ABQoUwOAp2qVRAl/eRJ7338OO\nC4+xZFgrDJiwCpcOr8LsuXMxfcZMvPS8jWnKfMaMGTj78BlmTp+OuXNnY92Bi7i0aSSqdf4T31Up\ngZoNWovPp1o7Z5bynLmYqb/ET+hb1PuiLCrUaQX9DVlGjR2NKiWLo2q9H+WSBPJ/iZkz1Nc0C/OW\nrpcLgRP7NmHi1Fkw/jvh3sUt4nUZT0/kFsu4YAAkIrIudlUbs2YAHNf9E7sFwNcPLssK8Lm4Ce/W\n6yfqndMMuyFV6hu1PmjodFlkBSz8pQoOP4rLrUOeKR9nLgC+wZCFG5TH3pFjJRB6yv2kCvXi1sbO\nLPnVoQKgtV3aNMrkGMCkjgGQiMi62FVtLNoAGPAS5T/Jgw/yf4xz97U7JXRo8AX+mr8RCPNBjneL\noEWzb5E1f0nxmEofAD2u7Ef5ovnR658tYgvPj99Vxkc1mspnAV9W/gItGn+L9Lk/lUsM+rb+GPny\n5YsylWgoz1A1EnkLoN62GX1hiF6mjN+ov/ooA0p901mpvFCkgvm7e0RlPgB+W6uB8n8/5bEc2oJI\nOo5YLStNUg6AoUF+GN6mHHQFquGNt34ba9LGAEhEZF3sqjYW4xbAMCXspDRs0erXsYOYT+lZFkP/\n1c4ULaG88d2QR9EbbwHcObEl2gz+T9Qhjw4j9UdfirpDBcOxYCNbfYoanWfJUfxFDYBB6Nywpngz\nnrb5rFxmEP7yDH6ZfViONBVy66INbeZFDYBLR/wkq7dRPtfexVPF68lboqZcoknqWwBdDQMgEZF1\nsavaWGy7gEvnSIFDD9StOCGYveuGtlDwxoy5i/BxCiUAyps4GAfALWObRQTAwPv7IwJgSuWNcvv2\n7WLaf+AwLlw1OkFC8Wfnz1CsWLEoU5U2f8lnGES3BfDplR1m35DLl1e30hlM6FwLT0KAeYN+FM+P\n2xFfpgFQPXZut5v+PNJQ5bGcsjZVMFMKjFprOC6RATBpYQAkIrIudlUbiy0APj2zEmk+rIPD8wbK\nJcDdI/PRdeIuUZdT3vjiEwAjHwsXEhL1zNy4ii4AqnJG+jo/1v9BVlK4L3TvVpMDYHSHz9F3xlE5\niolpANwxrA+KFC6MwmIqqDyWQpl/LB81uLl+Ivou1O6JrGIATFoYAImIrItd1cbichJIBuXNre2f\nS+UIKJBJhxPPtbqg8tj9YCAsNByTehfF2DXHxfI903/Ed7/NEfW59WOQvph2SZCOVXMjbwnDlri/\npxnOzowv4wAYHuIPw11jw1C6gXapD1Wn+l/h4eNHuHv3Lu7euIQxi9RLf4QrH59de4Li3raJ2Oym\nHesYs5fKxxl2Y5vyVh4z7AL2ePBIVkC7qh+Ju2voOVsADA3yxZp548SdOhJDWJA3bly9iJMnT8I7\nKO5n41rb/euXxGu4e9ddLjGPAZCIyLrYVW0sLgFwxe/1TU6q2DtHuwfsD93Ho3mpNMhTvDpe3zuN\n/+V5FwVLV8f1p37qNUTEczLkLIi3D48iZdrM2HPijvj4kvmziccy5y8nxgllHACfXDuAd9KnQrHP\nKmHq0i1yKTC0QUW8k+MdZMuWLWKKCDFeD9ClV39MmDkHyzbLu1+EPRevbehKc/eI9cOgXm2RI0cO\njBln7thFL+Uxw9a/76uXQ9YcH6Bb7/5Rdi87WwD09VbDrc4kxNrSq8sLUKn3SjmS/NzFaxCT0fUB\n/1fgPbEs/Xv/k0ti9lu7H3BbCedjen2PLecMIX1w58a4fv8RJvdvhtXH78qlGvXzxyS2x4mIKH7Y\nVW0sLgHQUcW0C9hS7fvOlpVtOOMuYDXkJFoAvLIIFbsvlCNNiwbNZGWwat50WQHffJQOK47JGxZH\nw23HWAz+13Cxbv33dO/4HPSZpm29VqnLjQ9OYAAkIkpc7Ko25twBUIe3b9/Cxy/hxxFGFh7ojQnj\nZ8iRLYSL17xnSnu0WBL9MYe2DoDnj+7C/EWLMXfePDF+5XEN8+bOhfsr9Wfpi7rVP0P+T8ri0gN5\n/zeF+vNWr6G4bsVCLFq8GP5KfffoXsybv8jkmoe9uzZH7gJFceFe1DA2ceJEs9P1p+pnM4gcAINf\n3xBfP0/h0rjzwrCz31ijojljPZGnfaVCmHrEsNWv2UdZMW/PI/xRvSjGbda2UKt+KfsuJmwwDYox\nYQAkIrIudlUbc+YAmJQlxhbAHrXyYfgmw9fo2Hu8mBvCzGulzixrbbl+C+D7Sv1Epq0+3+fH9H23\nRV0gp+Hi2urz43EzDRPmtgCqZ1kvnDBEfN7j93zlMiVSh4Vi0fgBqN486rUiI1s7pDGqdpogR0Dd\nQtnwz5qT2DmlHcq2NFw0/KePc+L3xQfkKPaAF9vjREQUP+yqNsYA6JgSZRew723oUmu/f587e/BY\nbli7dFI7HvLQjlVKsDENdPoAmEep9QGwd8OiWgAMfoKc+b/GX3/9Jaby5cvjyK032pOkvXv3mp2e\neJlu1TMfADWvL29ByjwV5EgJgOFh8PK8g7xZUqFCq2FyafQafF4IExeuxZkju8T3dPWt9o20qPYp\nRs1dhdMn9yG5svzEU3nDYwUDIBFR4mJXtTEGQMeUWMcAfpheh1PPgT4d28slwLkN0zBlrRYCdbr0\nYq6KNQC+voacJQx3fLFETAFQpctaWFZGQh9DlzPq3WWiE+J5FLr0H8mRkZdnoUuTXw40DIBERImL\nXdXGGAAdU2IFwPuH5iFf+W+x+oSHXKJezNrwz06nyyArLeTot4nlVuoXsv6lbmHM2K8ePxcknvPS\nsOEMJ9xfyip+Yg6ALzFpxzVZGwnxQKtRm+Ugdupr9ZO1MXX5G/0NoyXjn4k5sT1ORETxw65qYxkz\nZsSSJUuwePFiTg40VapUKVECoCpyeFHHP/4yAP2G/C3qAWMm4fnj26Jef0J7Tb/WKYSPKjfAjy1/\nws+NS+Lzpr/ipW8oFvRrKp6XKUN6ZClUWTw3ISIHwDal0iP9B//DsFFjMGfVTrkUqJxNh6z5iuPP\nv4fhx95j5FJgw8Tf8L8qjeTIwN/nBbatWYyyVb+RSzSBvq+xe91ylKlouLyMscg/o8hie5yIiOKH\nXdXGFixYgIULF3JysGnRokV488b0+Ln4imsADPRWb/Vn6vEL7fYuTz0eiLk599y1iyO/ePFUzCME\neMHjScK2/OnFtgs4ViF+cD8wH88TeBJKZAyARESJi12VKIHiGgAdkRoAq/SJdCHoePB/9Qh9Bxuu\nEWgpBkAiosTFrkqUQM4cAIN9PbFr+1Zs2LABL/2ttBkvAS4d2Stew9696u0Do8cASERkXeyqRAnk\nzAHQ2TAAEhFZF7sqUQIxACYeBkAiIutiVyVKIAbAxMMASERkXeyqRAmkBsCNGzciPDyck40nBkAi\nIutiVyVKoGXLluGzzz5DmTJlbDZ9/vnnIvzY+uskdMqWLRuyZ89u9jFrTl988YX8qRMRkTUwABI5\nsHLlyuHYMe22cY6KW+eIiJwPOzeRg7p7965T3Epwx44d3EJHRORkGACJHJS6ZU09/s0ZqLuBHz9+\nLEdEROToGACJHNAff/yBQYMGyZHjCwgI4K5gIiInwo5N5GCc9azXzp07Y+zYsXJERESOjAGQyMHk\nzZtXHP/njLgVkIjIObBbEzmQU6dOoWTJknLkfM6ePYsiRYrIEREROSoGQCIHkhS2oBUsWBCXLl2S\nIyIickQMgEQOon379pg1a5YcOTfuCiYicmzs0kQOQD2LNlmyZHLk/NTb5PXp00eOiIjI0TAAEjmA\nTJky4dWrV3KUNKhbAUNDQ+WIiIgcCQMgkZ1t2rQJ1atXl6Ok4969e8iVK5ccERGRI2EAJLKzpHy8\nXNmyZXHw4EE5IiIiR8EASGRHX331FTZs2CBHSRNPCCEicjzszER28uzZM2TOnFmOkq5FixahSZMm\nckRERI6AAZDITtSzfv39/eUoaUudOjXevn0rR0REZG8MgER2MHnyZHTo0EGOkj41/KVJk0aOiIjI\n3hgAiezAFY+La9y4MZYsWSJHRERkTwyARInsk08+EffMdUU8IYSIyDGwGxMloqtXr6JQoUJy5HoO\nHDiAzz77TI6IiMheGACJEhG3gAHvvfce7t+/L0dERGQPfDcisiE18K1YsULUv/zyC0aNGiVqVxYW\nFhYRhL28vJA2bVrs3LlTjImIKHEwABLZiBpu1KCjTu+++y7SpUsnH6GRI0eiWLFiET+fli1bykeI\niCgxMAAS2cjMmTMjAo5+at26tXzUde3bty/Kz4XhmIgocTEAEtmIeh/cyEFHP/n6+spnuZbs2bOb\n/XmoExERJR52XSIbUe/0YS7o3LhxQz7DNb3zzjtmfy6vX7+WzyAiIltjACSykcgBJ3ny5PIRatCg\nQZSfz/Tp0+WjRERkawyARDZw8eJFk3BTokQJ+QjpLViwwORnVK5cOfkIERHZGgMgkQ306dMnItio\nNZnn7u5uEgKJiChxsOMS2UDOnDnFMYDHjx+XSygm+pNDiIgocbDjks15eHiIN3c1ELnClCJFCvH9\nZs2a1ezjtprUYwytEaLUaxaa+/y2nDJmzIhUqVK51Hqifq9fffWV/KkTESUuBkCyuUePHqF69epy\nRLZkjQConqVrLzdv3pRV0ufp6YnatWvLERFR4mIAJJtTA2DVqlXliGzJGgEwR44csiJbunv3LgMg\nEdkNAyDZHANg4mEAdB4MgERkTwyAZHMMgImHAdB5MAASkT0xAJLNMQAmHgZA58EASET2xABINpeY\nAXBk569x0N1LjswL9fJA2Vrt5CjhQgITfj/fQH9fBIbKgRU5YgAM9X6AijVbyVH02tWpiPtvQuQo\n4bx9A2QVf97e3rKKm/g+3xgDIBHZEwMg2VxiBsA3j24hTNYxuXjLQ1YJ81vDz/HUJwybp3TH6ccx\nB8HqH6XHxP/uyJEyLlMSXsGA15PbqNS4v1xqHY66BfDSjQeyit6jO5dklTDh3nfw/S9TgTAflKne\nQi41dWDZSFSoUBHJlJ/Tl10nyqVKGDu4BJP/OyrqLl+XhrfMoX83+VT8TA1TNrE81PseGv08TtRr\nxnTG/jsx/9FhDgMgEdkTAyDZ3P/ZuwuwuJU2CsBL3d3d3d3d3d319tbd3eXW3d3d3Z0qbSmUAi3F\nKe56bjY7lF1YKFAoC5z3eeb/ky/ZbFboHQ6TTEL7E7CzyXUU67NYrCk7XcnEUli3N83CpMYVsOmc\nmbzu9+MRqvdfKS8rlcsUsz+CutoB/BvSq732PRPaYfdTS7EWzA9vf4R01tXfq2xqyz4WF9Bo7mFp\nKQDnHnxRFWVB6L7orLzUq04BfPeVFyX+0CtYWyxHHjuARBSXYva/PkRaxEYH8N7xjdi2ey+6tmqL\nr1+M8PqDMexN36FTq4Y499EF1qZv0blNfdz7Gog2VYsgXd6yqgcGBWD13AlIX6Seal1N1/bt0V5L\nO3DfROyhMrR+QSzY+0asATmTKfDeRayo8/mBddeMsKBesV8dQMBX7njcMfwJuJuhcqdJoh4z4rYD\nGIRBoyZgy6oF2HziLvRfvJC6UMCauZOQvXB5aSkAq2aPQ9asrWF064B8rlvOv5AfafruPto2rIgP\nzvLqL4YPtmj9TNq3D5Xw+VlJx0srVgCLJzuQunJXsaaNL3rMPCSWgdmdyqFw/YHycosSWeGhJUY+\nML0Dgk/vzYn50MtUWF7eOr4NHphG/U/B7AASUVxiB5BiXUx3AIPcjKGXtYtYs4ciSQ6xDDQvqYdL\nn1X/MW6XOxWGLdwrL09qWhIz96g6G3B8D0XWmqrlaKiaTYFdz23EGlAkux4OPQidNgFN24+U/39B\n/eJqHUCp6+FgLHd+ag+YLyoxJy47gFUzKuAullvlVeCprVhxewpFFtEBd/0APb3scA1SrrhI55tc\nLitlks7dMOp/SZU5fzgGRZrSYk16GqPb0rGLiTVNr+5fQQbpueZvvyAqKgMalo7w/cuSVdXhC7Z/\n8SB5f4NonjM7gEQUl9gBpFgX0x1Aj48XkSp/J7Gm2elpXyXjrw5g3zwpcP2Dp7x8eEZzjF97U16G\ny0epQ1JDtaxm7owZmKGlXX+r2bmrmVvqAL4K7t0ABTIpcE7fSaypbJ0+VCwBq5qUwr47IRHhpM51\n4Ovvh6xJpU5g79miGjPisgOYRXru4HdhSq+yOPTCSrXi8gmKTCVUyz8NoNCrr1qWqJ9v5WRhO4Df\n3p3T+pnMmLFI7KHibnwGinQhHUD7d9K6oopYC8v6yxP5ud86qq7E8f/5BUMXHsehJf/IdffQCaCv\nJYZsuSdWlAJRvVE/mD09Ie//1IwJIBHFL+wAUqyLjT8B1yuRBfom1riyYzoO3A8ZpxW6A3jNQDXm\n69D0ZiEdQGftHcDIWtilAhYfeCvWgDRSB+BnqA6DslMQug1ffgUuxpfRZswBsReQLokCNr/Gkv05\n5fP8qeh2AL8+OI5yrYbDxcUWeQtWE1WJssOt0QEM+fO7+vlW0tIBjDwP6VhpxDLw8ewclOwQ8Z/X\nN4+sgv1P7eTlVGrncWndKBSsO1qsqWwe1QReYlmpR6V0cBDLP42uQ5GsgFiLPHYAiSgusQNIsS7m\nO4ABGDh9u1jW1KFqJlwxUv0hsn9e9QSwGSauvy0vw8Xwj/4EDM8fSFVRNV5MSZGmgljSblmjkth5\nTZUYuhhdQeux++VlpcV1i8JZ/nNozIjLDuDOKeHc6sVVer+DO4COUmcwnASwitQBFH33aKmZR08e\nc6g0rkV+fHaJ+I3tViVkzuPkGu+bP0q0miOWVTLnriSWVLpVTPsr7VRSJNP883BksANIRHGJHUCK\ndTHdAfR3NZE7Durt2kd72JjpI5m03HTQfFiaSJ0OabnFsAVwsrNC9XwpkL50U3j4BmLd5B7ytpef\nw47bi6wnR1bg0NmrGDekm6gAti/OSsfNI9ZCzKiUE2tOhtwGZs3E3th+5CxuXD6BbRdfiWrMUL6u\nPxXdDuCIxqoxdMGtRB3Ve7N2Wk95/f3Xb9g8vZe8/OK9IS4d3iAvn7/7GF8MXsrLQ+dvkx8TXe27\nDsXV0wew8exzUYE83u/qZ2cg0FV+jqIVamNQ/wH4qXa7wCBPW7ToOgTXr9/A5H800z8/hzeYedJA\nrIWoX7extP91bJw7Be7R6MSzA0hEcYkdQIp1Md0BtDO4gNDhTqfOPcRS4qbs4Pyp6HYAJ8/eIJZU\n/EyuwTEG082Ehh1AIopL7ABSrIvpDmDjIsnQbcxCWNrYwNrKEnPG9YGhffRnf0hI4qoD6G/7BAq9\n3Lj9wgC20udi8OwGWg6aKbaSNuwAElFcYgeQYl1sXATy+c0jTJ48GTuPXxIVUorLBBABnti4ZhFm\nzF4Ec9toX82RaLADSERxiR1AinWx0QEk7eK0A0hRwg4gEcUldgAp1rED+PewAxh/sANIRHGJHUCK\ndewA/j3sAMYf7AASUVxiB5Bina53AN89uCJ3nB5+8xGV2PX05jksXDALr81/iorg/QM9e/aU25TV\nx0QxahJ+B9APY3o2hCJ1TrEe+2YM7q36XHp0Q/BFzSPF5xTcVhx/JLZEHjuARBSX2AGkWBcfEsA2\nxRR4ahGDU3KEY37vqlDNTQIs710ZZ99ZizWgb8v62Lhxo9xsVfevjrJEkQC66kORQfs8vzHNz/4V\nZq9RfSabD5xXFd2+oM2ACb8+qwndG+Bt6KlgIoEdQCKKS+wAUqz70w6gs5NqzoVA/+B5HkJq6rx9\nVB04N7fgLhbg6a45tUSAn+p2MR5umvWWhcN2AJ2cQ13J6ucJn6j/d16DRgfN+wsUScSNo30t0OGf\n1fAKeYnREvcdwCC4eYXuSAeFfS8R/Eb6i/9X8fDRfAO8fVTbNepOz8N0AD1cncVSCCfxHfEPjP7N\nCBuXyYcXn0zFmorlNzOxpFI2c2axFDXsABJRXGIHkGLdn3QAG5QoACPjL5g3sAEuGShnYw2AntTJ\nMZZquVMqcM7AHg7mBiiXTYEhK/ehU8duKJ41BTrPPYTBXZqiR+t6v6YhWzNjiNRByoDRvVqjR/sG\n0nIyua6k3gE0vr0DMzedwr3T26QOWia59vLECmw6+wDPrx9H+vzD5dovfnbo0qWL1qb5R+WgUB00\nn1/rJ7cuQMt6VeX1xoM0pyGLirjsALqbP8WA+Xtg9PahdB755NqzU4vRZuxavL57Qqqlk2sn9m+W\nz/PmpR3o1aevvPzl4yt06dUT6aXlXQ+/4ofxS+RLpcDkuQfQsUdPZNRToN0kMUtIqA5guUIl8NnI\nGIXSKLDyzEu5VjxfXhh/+YKBdaT/D5WmrhrYW+tnNWnNPrGHEOSBwQP6IIV0TgpFUjj6iboGP9Qc\nsFYsRw07gEQUl9gBpFj3Jx1AhSK7WAI+m/xEkMtn5KzaR16/PLUPek28IC8fWNIVvZefkpfh9VV6\nXEnVsiSkU6ScCiypWAaG1MqDMZuvy8vqHUBFhtLw9vKCl5c3CkuPXXjVDH3KS51OZ1Ua9epq9Kdv\n61cnB1Zd+CQvO397IJ1PGnlZXa7UCux5oJk6RVZcdgAPzKmNI0++ycvfXj6V/39cvXx47aBK79TP\nTX25Z4N82PzESl42v/4fyrWYLS9v61oXkzY9k5eVlI+Rszy1DuDttUPw0t5P+qy84PTu1K/j6uWs\nKv+/0lenP4xVJde2ToMiadhp/h5v/Rdfo/nnenYAiSgu/fl/LYh+4086gOe2TJT/oz56/jpRkbia\nYcnqrTg5uS/6TFXdCPrI0j4YvPasvAyPL9JjyqmWJSGdDWdpOblYBvSPr0SZvivk5ZAOoAeSFmsk\n19T5uX+Tj1OoUpNQqZ7E1wqVK1fW2rTNT/L13WPcfPgEJxf1RbV+y0U1RKDNUxTvvVqsRU3Ia42+\n6P8J2BeZkiqQNncxWInOstL+DUtx7+U7jXNTX+7dsDjWv/ghL5tcX4PyrVQdwC1damHO9jfyslJG\n6TGOygW1DuDIxgqYaUnmNk/uLj/HnI1HRSXElMZ1tH5Wvab/J/bQLkuysO9tsSw5xFLUsQNIRHHp\nz/9rQfQbMXERSPd6xdFg8Ep4f3+OAu1UHQRlAvgnHcDXx1agy4rj8nJIBzBA2ickJVQy/qJKp5Ru\n7VsmbU8i1v6Ev9p5hRL4HctOvxUrURPuMaPgTy8CMX5+Xj4P5Si/TqUywUVV1jg39eWIOoBzt4e8\nD2nEMdU7gJO7l8SsA8/lZSVfZ/WxoQFoUCwTBq67LNb/TK18RcRSMHe0m3hALEcdO4BEFJf+/L8W\nRL/xJx3AGTuCp3rzRs46w3B100gUajBCrvSqWRS9p5/HF3M7HFzQAkO2XJHrCPohdTBKq5YlIZ0N\nZQcw5CtfJ7cCbuJahNb5pQ6gpWq5Zs4UyFasNr59/47TW2bAwj0Qsye3V22UZFE7RvR4I1PKFFL3\nIcTIoUMRfOnEiBGjxVLUqb++6IpuB/DE+iliCehRUiF1v4Dk0vkYOAOuVu81zk19uX/TYtj6Ws72\nYHV/E8q2ni4vb+lSH63Hb5GXATekL95Gtej1Fop0RVWLP57Ix9p64hq+fzdFpz7Kx/pj52XVn9nh\nb4Qaw8TYwSgye3EcB6+p/tyvf+MwXlmFXFykdGV5H9j/wV+X2QEkorj05/+1IPqNP+kAjp8yERWK\nF0O9Vt1FJRCVimZF51HL4fvjNspUa4ogbzMsmLcA8+fOx1cbZyxfsgiLFi3EpjO3cWDbaml5EZav\n3iE91g0KvXSYObADCpYqh5dmqitTP10/h3nSPgsWL4Wr6BAO79kaabPmx8233+X1/RtGo3WDuihQ\nqhLeWERz0Jdk3bq1uPU0bLr3/eNjTJsyCU/fa15hGlVx2QF8cnEjendqgXwFi+LkA1UH7N31PUiR\nNC0+2vmje7WiWHv6CU5uXyc+k7UwfnoR8+ctxPz5C2FtZ4OFC+Zhwfz5ePHZSk4Apy06ijLF8qFV\n96Hy8ZSWL1V+vguw/cQtef3TwzPInSU9OvUdI68r/xQ9acIElCpaAC27hbpgJyqCgPXL52PV5v2i\noGn7f6vEUvSwA0hEcYkdQIp1MfEn4JjhInWQQv4EnBDFZQcwpm0NNQYwoWEHkIjiEjuAFOt0pQNo\n9fG63EFy8dB2aUbCkFA6gP5+3mhXIj2aj9j4R/fx02XsABJRXGIHkGKd7iSACV9CSgATOnYAiSgu\nsQNIsY4dwL+HHcD4gx1AIopL7ABSrGMH8O9hBzD+YAeQiOISO4AU62K6A/jt0ws8two9t2xsCYKj\noy2MjIxg7RL9q3//lL+3k3wOP+wcEBDBkDhd6wD6e7tj/8bNYi32ebm74esXI/m9ikvG0vMbGRnD\n0VX9Zj+a2AEkorjEDiDFupjsALq7OKB2oeTY++GnqMQ25fRxTcSyytA2qjl79dLkRsh8F8CW6YNw\n/dFrvLx/CSMWhZpXNhwFpOMoj6VsbuJgWdVqylao5XjVBsmscjnxKoKXrtz/T8VkB9DWxkQ6p5i4\ncXbkTG5UHIbqfa4gTxzfukTr+2Lz/MCv97j+yA2i+ns59BRQ/wgKZ1AdI232ImFmflHkqSmWwmIH\nkIjiEjuAFOtiOgFsVzXPX+4ANhXLgMGdI2IJWDGoLrpNOa9a8bdFnsrTVMuSzgUVYaeMC8X9y3WI\ne0//4u3wEU+/Oos1wOnZdjy3C5nrbHrFvHgdjzqAyvs2xsQ5Rdb05mXxylrczFGNtnNo33emWIq8\nTdMHIp90rOD5RkZ1qPWr0ze2TRWkKRLyXZG/O4XqieWw2AEkorjEDiDFuog6gL6Oljh7/iKuXLyA\nn57K/3D74PyFi7jzykTevnTiUJQrVwXX9FXrSqoOoCNM3zzBhUtXYWrnhgBPK5w5ex4PXn8RewG3\nT25D+XLlcOjqC1EJcenscRw/HrY9evlN7BFMswOo7szsTvjiGjwVhGpqN0sPVedDkSK3/P8RKZ5G\ngXTZ82PH2UeiElbVPOnFkkpcdwCvnD6Ba9eu4eyla/L6yzvncf7saXnZ3fozGlapjA79x8qzgKiE\ndABPnTwpP1bp0ZXzuCIte//qq3liUKdmaNFziFgPEejpoPWzUrbQItsBtHi8X6616hpyg+nf8TK9\nj0c2PsghPS64A+jsq/Zcvt+gyFhdrCixA0hEuosdQIp1v0sAX5+cjwwNx4o1qdM3pq/8/4Pr5sBF\nE1X6pf4fcPUEcHafMlh+8b28bHJjEwp2mCUvH53XDeZi5q6S2fSw97G5aiXKtHcAPz27ggIV2ok1\nlR8vT8nn2ajzP6Lyey8fXkZ6PQUqdpooKuoCUa3vYrGsogsJoJ7GcwTg+Dtld0g5h7Kq/urkHJTo\nNEFeDp0Aqi/nll63pTz/XRDyle0g11w+XoQiSRZ5OTqikgC6Oljiny6N5G3B0/BFpMOYtfL/55T2\nV59xONj7E7Nx4q29WFNiB5CIdFfYfxWJYlhk/gSs/h/opcdUid33d0/k/zd690rudAT/Z129Azhv\nYPVfHcCvN7ajQEdVBzCJQg+LFy+WW896NdBs7Ea5HszokwEMDMK2b5Yhf35V0dIBDPSHi+031C2R\nBSkKNBJF4M3Fjbj78rn8WvrPidwYwGDJpMd4hrq448XBKTAOdd2JLnQAT8zvho4zD8nLO+eq5mVW\nOnftgfS/Pti3bCLStgquh98BzJ9S1QH8cH4Z2oyeIT6vBShWrJhagih1D/08tX5WyhZaVDqAwY7N\n7YvGY9eJNe1Gd+ksloBc0rG8xLK6YnX6iKVg7AASke768/9aEP1GZDqAy/pWx/C11/Hy2ApRkbiZ\noe2w2fKi8j/gUekA/q4j5OnpAQ+PsM3bR/2yDiXtCWCwX8/jY41MlYP/fBkodUAV+Ba6RxeBNb1r\nwC5Uv6VMzrAdMd0YA+j763nGrVT9+Vdp4+Q+eGJoDfs3Z5GmZfAcvL/vAN7Y3gELTxqKqhZBgVo/\nK2ULLTodQMAbuTpoS2AFpw/y40M3A7UYsF+X9mJJHTuARKS7/vy/FkS/EamLQPwdpf+oZsLYGatF\nQZWKBVP+Bze4O6XsAO776CgvzxlQDSuvfZKXzW9tR8FOqg5jdj0Ftt0OGTe489BZsRRVEXcAs5Ts\nJP+/t9l1lBu2VV5WOjqlA17ahFy88TsdW2peaazslLScsFssh9CVi0D6V8uG8TNn/UrCvMxuIGv5\nQfKyw/uLSNP69wlg3hQKWEkdQOsXJ6BInVdUpf6e46dI/UlWm+h0AL1Mb+Cp9e8u2QmRO9Sx5o0K\n7uwqBcDMKfjs2QEkIt315/+1IPqNSHUAJXUzKWCvFsCVTqVAkz6TMHnKNLkzOHXBUvj6eKJgagX6\nLj4h3w/vw9nlUGQqgQ2r5uLAhkVQZKwEY3Nr2Bhclf+jnzNfQaRLkRxhs6LI0uwAruhWCcmzFMG6\nDRvRvmMPUVUZ1LAUnhiYwdHGFB2HTJdr3g7GKFC4PEJ3Bb2+35DPb/m69Zg5e9avzm2wc4t7wlpL\n/1FXOoCBTgZIXaW/WJM6bS6f5OfevGkDpowZAr0MJbHv6HXYflX9SdzKSXXfRuUtbkbP/Q//jl+M\nYhkUWH1QdVFIuVyp5f3y5s6J1v9o/rk+KrR1AJ1szORjm1qrfmlQSietN+g0EFvXrcLxWyF/Sh7T\nvSEW7r4l1rRLIT02+Ejdq+eXj63eQrADSES6ix1AinWR7QD6uIft2ei//iD/v8P38G/sa/rpnTxm\nzNPRVlX4JRD6+q/EcnRFnABGxudvFpi2+Z5Y+zO60gFUcvbSzOncpI6Wg7gK+r3hd/n/tdF/q+pw\nGZmEXLGtZGZkgB8ObmItesJLACPL+usnLBhYV6z9KXYAiUh3sQNIsS6yHUDdpOwAthDL0bNq5qgw\nCV90za2cT2c6gLpoerMyMPyDSWKsTN9g46lnYu1PBUJRMPzOJDuARBSX2AGkWBe/O4B+OHz4EPbt\n24cHX6xELfICA0JfVBI9Hg6f5HM4eOgQnCIYrpbYO4Dv7l/Dful9Ur5XcemQOIfDl2+LSljsABJR\nXGIHkGJd/O4Axi+JvQMYn7ADSERxiR1AinXsAP497ADGH+wAElFcYgeQYh07gH8PO4DxBzuARBSX\n2AGkWKfsADZqFDJjBsWemOgAZs2aVSxRbLK2tmYHkIjiDDuAFOusrKzkjglb7Dc9PT3xrkdfyZIl\ntR6bLeZb584hU8wREf1N7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwA\nEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AAS\nERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIR\nERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhER\nESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERER\nJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIREREl\nMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy\n7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLs\nABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwA\nEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AAS\nERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIR\nERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhER\nESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERERJTLsABIRERElMuwAEhERESUy7AASERER\nJTLsABIRERElMuwAEhERESUy7AASERERERERERElYAwAiYiIiIiIiIiIEjAGgERERERERERERAkY\nA0AiIiIiIiIiIqIEjAEgERERERERERFRAsYAkIiIiIiIiIiIKAFjAEhERERERERERJSAMQAkIiIi\nIiIiIiJKwBgAEhERERERERERJWAMAImIiIiIiIiIiBIwBoBEREREREREREQJGANAIiIiIiIiIiKi\nBIwBIBERERERERERUQLGAJCIiIiIiIiIiCgBYwBIRERERERERESUgDEAJCIiIiIiIiIiSsAYABIR\nERERERERESVgDACJiIiIiIiIiIgSMAaARERERERERERECRgDQCIiIiIiIiIiogSMASARxRk/Pz+Y\nmJjA2NgYX758YWNji0QzMjKChYUFgoKCxE8SERERERFRxBgAElGc+fr1K5IkSYK8efOif//+bGxs\nv2l9+vRB8uTJUa5cOXh7e4ufJCIiIiIioogxACSiOGNqagqFQoHu3buLChFFRBn65c+fH2XLlmUA\nSEREREREkcYAkIjiDANAoqhhAEhERERERNHBAJCI4gwDQKKoYQBIRERERETRwQCQiOIMA0CiqGEA\nSERERERE0cEAkIjiDANAoqhhAEhERERERNHBAJCI4ky8DQAD3HDtzH5sWLcGq9asw9atW1Vt40Zs\n2LgZ1+5/ETsCgR4/cOrQNmyStin3WbdmFdas24hz9/QRJO/hgxWjK6FAsWqYMHcZli9fLrcrL43l\nrRoCXfDg2ils374dF249h58o/+LvjON71/86xpjezVGgQBGsVjuf+MTd6gvOHN6N7bsP4vknS1GN\nOj9HU+zdIT6j4LZ5E27oa3mPdRwDQCIiIiIiig4GgEQUZxLCCEBfq5foWD6L/DqS5auD9+5iQyiX\nVgxG7nId8c4qdGznjeld00ORoQae2wSKmib/nx/QsHgGNPx3h6go+WJCs+Io1mQMHANEKZQ76wfK\n5zXz2idRiTkWX4zg6quKMGPap8srkCFVamy+/01UAK8f91AsUwr8s/mmqESO94/HaF61KDp07Yqu\nam3E9E1wEfvEJwwAiYiIiIgoOhgAElGcSUiXAD85OBWppNeiUKTBsjPvRBXwNn+KeuXLYun5kJqm\niANAb4vHKJVGgWQl+8DKXxSFQPsXKJlegQzlusPSVxTVxFQAGOBihfN71qBtywao36Q9Vu85BwtH\nH7E1Zj3eN1U+546LjopKiNdHVNu6zwu7TRsfq4eoV60m9twzCjtaMp5iAEhERERERNHBAJCI4kxC\nuwegz4/nqJU3tfyaGg+YjdWzBqLj2OWIOKaJOADc/U9z+XitJ6wWFXWumFy7kLx93qGHohYiugGg\n6aubmDm6LxrVrY4BE+fh5gsTsUXTp1tbUbx48Si0yth2NoLLkT1M0KRIGumc02Hrxa+iGML583Xk\nSKqAIlVNPP0RcaQX5GGOf9tVR8niRZAumTKYVbWcJerh4B0jsVf8wwCQiIiIiIiigwEgEcWZhDoJ\nyKq+NeXXla1oc+jb/y6kiSgAdEGfZqXkY41adlnU1PlhQfcS8vZ6iw+JWohIB4C+dlg2vL60bxr0\nmbYDrqL8t7kYnUCRtAookpfCmY9hL9D1+f4YlVOqgrzjT0MuD44sZ/M3GNSinPz4HDX644eX2BCP\nMAAkIiIiIqLoYABIRHEmwQWA/rYY164eZh14AqNbW5EhiTKsSoe5x5+LHbSJKAAMxPpu9eT3qM8c\n9fv/BXPEuPIF5O0zd98StRDRGQHobGGATfPHoFm9Gmjboz/2nLkPz1CXHgf7cH0j8uXLF4VWGptO\nhT/xRtDPd6iQN5l0zrlx8L6NqIZweH8Z6ZTvqV413DfVcs1zJD3ZOxVJpPdl7r7bohJ/MAAkIiIi\nIqLoYABIRHEmIQWAN7dORZXWI/BNbVRZgKMhWpZJL7/G6j3nw0PUNUV8CbCN/jFklB6fue0UhJ5f\nJND+NUpm0kPy/A1g6BZ2Qo4YuQdgoCeeXTmC4V3boVLtWhg9cx1ef7UXG2Pesfmd5HOevPu6qIR4\nc2qKvK3JiA2iEj0ehgeRP18OnDYIZ8YWHcYAkIiIiIiIooMBIBHFmYQQAP40vI4GlSti653wR7Zt\nHt9Cfp1Js5fE2RehR7ZFHAAqmd/fgXTS46cfeSMqKrvGNEHSbDXwylb7aLiYmgQkNHuzNzi09yis\nvbSf758Jws7xzeXLgF85iZKSvyUaF0yGKr2XITJj/8zfPsCRoxdg5a55jmbPzqBf3xF4/k17HKvr\nGAASEREREVF0MAAkojgTXwNAF/O3WDZ1LFo3rIu69RuiWfOWGDF+Bq6++S72EDytsHPDAgzo3hbN\nmzeXW8MGddGyUx8sWnUSP+Uky+e3AWCwd3fPYeXi+Vi4cAnWbt6LT9/D3idPXWwFgH+FrwPO7N+C\nxfMXYunyZdh94hqctFyK7Gx8ESVTqC613nM7ZLISFxtzbFk+Fc3r10aNWg0xdPwcnL75Au5hB0rG\nKwwAiYiIiIgoOhgAElGcSaiTgETN70cARle8DgBJKwaAREREREQUHQwAiSjOMABU8sLYlsoRbHmw\n88pzvHjxQm5mNurXv0ZSoA+MPr7+dYz1oxrI7+/4iwZiB4rvGAASEREREVF0MAAkojjDAJAoahgA\nEhERERFRdDAAJKI4wwCQKGoYABIRERERUXQwACSiOMMAkChqGAASEREREVF0MAAkojiTUAPA74Yv\ncf+1IQLEevT545P+PegbWYh1SuwYABIRERERUXQwACSiOJMQA8BADzss+KcDmncbiy/2fxbQeFh/\nwIjOjdB1zHI4xmHW80P/HAYMGIzNe4/jxImDmPbPYKw/8lBsjS5vzOxeQfr8c+CqoZuohdg1sZn8\n3Qi/pcaO+2GDUaune5FOy/7tZxwIFcgGwtbsE64d24n+7ZuhRO7MaDpmBYLEVl3FAJCIiIiIiKKD\nASARxRleAqzjAn9ieK1sSFW4OexCJWPLupWCImtlvLHzF5XIs313Cc3qVkDxfCmgSFIat4zdxRal\nIDw+sBgD/lmCrw5eoqbOF2NaFEezsbvEegizuzvRsvskOEVx6KX+6bny97DpuLWiorsYABIRERER\nUXQwACSiOBN/AkBfPL11Dls2rcKqpauw/+wdaIumlFyd7PH5xXUsX7cVxj/9RBVwc/oJ49ePsGnj\nWnxyUCVUho8vYvbkiVi5/TgcfOWSmgA42dvi2Y3jWL36JJx+k7P5e7vD6scP/IhCc/X0EY/WJhDr\n/mkgfz59d9wRtRA2z04ii7QtXfVhcAgUxd8KxI4pvTBgyQlp2QU9yytH5xUPFQBGfLCry/oiW+nW\nsAz1flm/PIxsSRVIk60AWncZiP+2n4S1R+SSwMdHZjIAJCIiIiKiBI0BIBHFGd0PAH2xY0RdKJIV\nxUPbkCFwflb3UTSFMrwqiCOP3sLoqxX8A4Pg6mwP06cnkSmZtC1zdTywUKZ2QXBzdID1x4uolF2q\np82GgeNmY/OekzC3toHJq4uolie5dKx02PvATPUEAb746WCPB4cXIon0/mQpNARWv8my/L09YG1l\nBasoNDevCAJA+7dokEl5XnrYe8dYFEMEWD9F7azS60mSGUff2Ilq+LxtX6JLw5Y4ayD29TZDt3La\nAsDwuX4+i3zpcuLI25+iEiwAjra2sLQwxeMbp7Fg0hCUzJZK/m4pUufHhosfxX7aMQAkIiIiIqKE\njgEgEcUZXQ8AnQxOIY10fplrdoZ1qABuRw/V6LipO16KiuBqhFbZ9KDIWAUPLEJGAMLXAm0rZYAi\nWW7c+qY5fG3rrL7ysbptvyYqKq5vLqK4VE9baPBvA8CY5m32GIXSKQO6fDj72kFUQ/hbPUGdbKoA\ncOtLS1HV7sGeOeg0aDE0Yj7fb+guAsA7Jp6iGIEAB3StnAtd55wVhUjwtsCguoXk93b09keiGBYD\nQCIiIiIiSugYABJRnNH9EYD+2D2ltXyOk/aoBUiuH1A1RxKU6zofHqGvWHX9jOZaA8DvaFspPRTJ\ncuJKqBFvm2f0lp+j547roqLi+vpCpANAz5+WeHT/Pu5HoX2zdRaP1iLQASNqZ5PPa8P1D6IYwuH5\nGeSVtunlaQRDF1HUYv2gClAkz4AypUuiWLFiIa1oQaSTR1EmR54CReTa8Ucm4lFhHZvZHvlqD0DY\n6UJ+w9sYDTProUH/TaIQFgNAIiIiIiJK6BgAElGciQ/3APS2/oT/VizAtTuPcO3iOZw+fQYvPnwT\nW7VwM0LL8EYAVlaOAIwgANypGQC6vbmIElI9LkYAKv18fwoZpOev2mulqIS4vLqbfM7LzxmISlTZ\noVcFZQBYEo8jHkAI66e7kTt7cVz/FtE9C7XzMLqCelWb46lN+GFZcADYbPw6UdFdDACJiIiIiCg6\nGAASUZzR9QDQx/IRciVVIHWOkujYpQu6hGrd+o/EoSuvNKatcPt4ERn1lMFWfpx47SiqQIDFPZTP\noKynxbaHFqKqFIj5A+rK70PdUZtFTeXD6WVyXS9jQ7yLYLBebPJz+ojOVQuh7cRtUMVNPji/fiyK\nlWuKO180h/75eljgv5ljMWrmGvxw/U1Y522KDsVU79N1o/DvARjkaYqGRbJixIa7ohLWs2ML5Pcp\nX/lGWLvvDPRfvsCdiyewZMZ0bDp2F6EmMA7j8oah8uOzthgHj9/tHMcYABIRERERUXQwACSiOKP7\nIwD9cOXENpx6Zgg/P78wzdvNEbcPzkWllgNg4QO4uzrB3tEFvvJ2X7g6OcDRzQveHk6w++kMH19V\n3c3FEbaObvD1cYetrR08vHxUx/N0k9Yd4BsQCDdHezi6eKiey9cHTg52cHUPM1XwX+Vsbw1r+/CT\nyKBAT9w5dQB7Tt2CR8DvkrQg+Pur3segCHb1l94vH9/ITTMcFOAHHx9vRHawpK+XO6ytbeDi7imf\nh6+PJxzsbODgHOULjf8aBoBERERERBQdDACJKM7odADo8QX1MimQt84wmNh5iaImXwdjTB3aAyuO\nhj/BBFFMYgBIRERERETRwQCQiOKMzo8A9HPD6V3L0L5JTWRMprxcVdUy5yqCFp2H4cxjY7Ej0d/B\nAJCIiIiIiKKDASARxZn4MAkIkS5hAEhERERERNHBAJCI4gwDQKKoYQBIRERERETRwQCQiOIMA0Ci\nqGEASERERERE0cEAkIjiDAPA2BAI+2+fsOu/eejWsSkK5G+IJ999xLaEyhffvxri3QdDuGidMdgX\nttaWsLS2gZ+o/I6jnTUsLa3hFbkJiP8aBoBERERERBQdDACJKM4wAIxFfiZomV8BRfIqeGYZ2dgr\n/nh1ZSuqFc6K6i374/qzz/DREtSdXT5C/n4pFEmQKnVqJJWXlS0pek3dA0+xXzCb91fQvGZldB88\nGtNmzESPVtVV+yfJhjUXP4q94hYDQCIiIiIiig4GgEQUZxgAxiLnD2heMOEFgLf3zoCe9J2p1HMG\nPEQtLD8cmDsWq068EOtqfKzRv04B+XvXetFhUQQcvxvi8SsTsRbC68tlZJL2zVO9B1xFLS4xACQi\nIiIiouhgAEhEcUa3AkA/HF4+CmXLV0OLpk3Qum1r1K1cCrkyN8KN925iH8DT2gCD2tdCyYo10LxJ\nE1QqngfJ02RBx5Er4CzyGGfrL9izchpqli6KNEkUGLzpClwspMe1qKwaUSa1jEVq4uwre2lvPxyT\nnjdvhpRiWzK0GrMKbv6qY1m8f4jZI3uiaP48UCRJgyOvzHBp/XjkSJvs17GqtRkKAxsv1QOCRRQA\nBrlh26wRaNC0PTq2aYbyhbJL+2VAhyGzYeMeIHaSzszFFGO7NkC1Og3QRHqtbVs2Q5lCBVGp1mw4\nR3RprLcF/mlSHcWKFYt06zR8OkK9glC8saRXJfn1tpu+X9Si5/S64dJxkmLzdSNRCd/zw3PRtPM/\nsNaRrI0BIBERERERRQcDQCKKM7oUAB6b3Qwp0tWHtVgPdnTeWlx6oqoGWN9D7iQKKLJUxndfuSQ7\nO7WX/Dr6zLkiKip75jWQ6/UHLICDW5CoAoE/HqFcamV4lxPrL+iLqsqRxd3lx3RZdlRUlLwwrKVq\n1Nrg5Wc07mNn/+4kiqVLIm3LgL33vomqJJwA0O7lYWRJmw3HPmmGRz5GF5FbOr4ieR5cNlVeHOuK\nbqVSoEbPLaodgnmZYvnoTfjhG/J6/oaHW0bJr1+RuhCmrD6Ip2/e4O6tq1gxtS9yZc+BjiPnwtL9\n9zfs8zK9jLwpU2Hx2fAu6Q3AhxfXMWNEb2RKpvyMFChQtiH23vwstsctBoBERERERBQdDACJKM7o\nUgDo9u0JGhRPK59PntI1sXjzMVi7hB+weNqa4tyhvTh+7DBmd1UFff2nawaAhxargsGh686LiuBu\nhLYlFFDolcNDc83nuLBlmvyYOvP2ioqSEwY3UwaAKXDwhZWohbi2c6r8mPKD14iKRGsA6INpbXMg\nab5y2HTwCI4cUWtHj+H0mTM4c+Y8Pnx3lvd+dnYFMkrHVR67WpsBOHzlOSIVOflYYXafDmjYsGGk\n24gZKyI4tj8m9qgjn0ePKbtETY2HIermTS1tz4SDT21FMaxnR+aiZt2+MFELY3/H5MlpVM2j+l50\nXXZOVOMOA0AiIiIiIooOBoBEFGd08x6A/nh5+yj6t6kmn5siaRasvfRebPPB/hndkCF7Fdz+4iRq\nwIXJqqCvX1QCwOLKALC8lgBQFeZFJQB8f2K1/JgqEzaLikRrAOiJqa2yQpEkF06+cRS1sAL9xfXH\nQqC7JU5tn4c6xTLKz5OpTDO8tgo9hUbsury4r/zc3SbvFhVNBxfUk7dP33pDVEK4Gj9E7/Zdcfql\npahEjafJZWRNJr2/Q7eKStxhAEhERERERNHBAJCI4owuBYBPrm3Gix+qkW8hXNC7WDKkKtNCvj/d\nxXWD5fNtM3ufarOwb2RruT5wpgifglQjzA4v7SnXR2y8JK//4mmC9iWVAWAlPLXQvD/fpe0z5MfU\nXXhAVJScMKS5MgBMiu2Pf4haiEW9SkGRpiSe/VALhNwM0aqIMgCsipfWIff1+3B+FVJLx1cky4ap\nWy5rXE4MfwdsnDsZtz7/lJbtcebcVoSOCT0/nkE25XswS3sQF2uCPLC4RznpPciOy8ahp//wxrAq\nWVCgyTi4ql0F7Gv9HkM6tsaCQ/fh6e0FZ2cnODmp2s8fxlg9bSz2XQsOdwF3F3expOngzI4oWK8f\nrMKfdeSvYQBIRERERETRwQCQiOKMLgWAt3Z0k8+lSMWmmDRjHubMmYcu9UujXP2BMLBX3fDPx+YD\nOlfKJu+nUOghX5maWHXiFh4cVIV2ygk8OoxehS+WBjiwcQ4q5k6LZMmSIUuRmli04SDM7Fzw+OYZ\nTBvaEWmkerJkSVCr8zDsuHAftvZfcGT7UjQolUt+TNocpTB16U58slWGPK4Y3KIwFHqp0ah5C3Tr\nOQjz5s7BjPFDUCx7ZrQfvU5jRlzDWxcxeVA7pEyifI6kqNNlBLadu4ngK1+97d5jaNuQCUmCW5WO\nE/E9eGBfkDvGd0wn1VOgSft+mDdnDuZOG4fyBXKh2/S9v5mwI/b42LzHxEFd0XfUdGzZsgV7tm/G\nzNnL8PxbyEQt/k4mGFqtoPyakiZV3h9R83UGt3bTQkZZWr+/ho51ysr1JKkyolSF2ug5eATmrt4F\nC3fNEZFxiQEgERERERFFBwNAIoozunkJsC4KvgQ4OQ7p24kaJUYMAImIiIiIKDoYABJRnGEAGFmu\nGFA/i/RepcDBNz9FjRIjBoBERERERBQdDACJKM4wAPydQHx8fBvHj5/A2fOXcOnSRZw+cQInTt3D\nT8/Iz2RLCQcDQCIiIiIiig4GgEQUZxgAEkUNA0AiIiIiIooOBoBEFGcYABJFDQNAIiIiIiKKDgaA\nRBRn4kUAGBiAgEBebku6gQEgERERERFFBwNAIoozuhoAmrx5go3zRiKVdG4KRVaceJnAZt4NskKb\n7NJrS50HZ+8/xdOnyvYcFrYeYgeV93eOoW+H+siZLTsKV26M6St2wcI1QGwNy8PSAJtXLcKypSux\nZMkSrFy+FEtWboORva/YQ10QfL1c8P75fezbsg7/9u2IfyYek6oxx9/JHLtXzkXzOpVQtFgxNOzQ\nB2v3X4CTn9jhtwKwfUx7FG/UE4aeoqQuwBkXdq9Gt5b1ULRoMVRu2AYL1+7F15+hXq+fKwxeB7/P\nTzGrcTn5e7/zmZXYIfIYABIRERERUXQwACSiOKPbIwAdMLJ+eun8cuG0fkILAG3QvVhGpC9SAz98\nRE2N3efbGDdmEh5/DZlx2OjhQZTPmlJ6P5Jj1Zl3ohrMD8t6l0O6aj0QJifzM0GpLArUGbsdgaIU\n2qutM+XvQYOeO0XlT3ljSf+ayFWqKXaevomXL1/i2b3LmD2oFVLLoa4CXWYcRPhRJuBh+hDNimWF\nXlI9KMo3xzt3sUE4u2IwsuQqiaU7TsnHf/n8AdbOHozsaVTHL91+MhzDeYJjw9pK++jhgL6NqEQe\nA0AiIiIiIooOBoBEFGfiKgD0/PkNj27fxp17D2Bq7SyqoQTZY3jddNL5xWAAGOQJg5dPcOfufRhb\n2otiHPhNABiec6t7Su9HDpx9H+rc/W3Rv0YaKJJmxsbrRqKo4m9+Azn0FOi08FS4o/tebpwufw9i\nKgA0enUH9wxsxZomX+l88qVUIHnRejDWNqpPcmrZEHSbuBPeLsYoogz0SjbWCAAdP7/A7YcvxFoo\n/hbokCeF9HqK42Lo1FBgAEhERERERH8bA0AiijPRDQCDAv1hY/oOg5sWkR+fsVQ7GDt5wv/XELMg\neHu7Y2qr2mgybA1+eipTrkCcWztE3n/MxhvyXr4/zTCwXl65NmnHbbn2i5YA0M/HA/bfXqF5QeVI\nOAXm7Hgv15X3CfRwtcfeaR3leuGGvTRGwhnd2IoSuYtg9cnHCAgCvH+aY3L7yvK+1XvPhVt4Q+Mk\nnjZfsGf9eqyPQrv76rN4dDiiEQA+O7wI3YbPRbiRk7cDFvRuJL8m5aXF87fsxdp5I9Fr1HxYuIp9\nwhHTAWBEzG/+h6TSc80/9UFUQgS5GGNgq8bYc89cXvf7/gz5U4cNACPiZ3EfefQU6DDzqKiExQCQ\niIiIiIj+NgaARBRn/nwEoBemty4uHSMlFoe6LNXg/DqMnLEt5LLTIE9sGNUcFer0h4lryFi023sW\nyOdQePhSzRFq4Y4ADMS6NsrwTg9L9n8UNRXTRyuQTDpW+VZDpTNTMbq4EnpJs2D1JX18+PDhVzN8\nex0NcqguF201/pTY+y+JZADo62iG3ZvXYNyQriiaV3k5tOp8u887Es5oPj+c3jAbXdq1RsG0qn0r\nNh+C1z807y0Y2t8KAB3eHUfBHAVx+IW1qIR4f3E1WvccB1u198PPIooB4E8D1C2UB1P3PxMF7RgA\nEhERERHR38YAkIjiTIxcAuxljrYl00rHyYz9Ty3lkovhRQz7Z4bWe7A5GT3GrDGjsGTzQRhb2uH5\n+Q3yORQbsSzSAeDa8ALAh2EDwN1TykKRIh9uRvMqYlczfUwbOhRDo9AOX4s4gIruJcDwMkP7Uhmk\n154eG66YiaJSIE4t6QZFqmJ4bBXyplu+Oo+a+VTBYcORaxHeU8V+AOiPbTN7o9+UbVru++eFJUOb\nonTtTlixcjkWLVr0qy2YMgIZkyugyFYY/86Raqu240voCT6E+7vnomXPybD2F4UIMAAkIiIiIqK/\njQEgEcWZmLoHoNe3+yiZTgFF5mp49sEAU/8Zjtd2mlO9+ti9RvXMCuSqMUTjMtYXZ9fJ5xCVAPC/\nlpXkAGfZAUNRU/n+bE2YAPDcuqHy8St3nwdt2VCQtwv0H7+NcEKKGBfdAFBybml7KNKWhL59yKtx\nfHceKaTX2H7sdlHRtGFIWSj0MuPsF+0jAWMzALy7ZxH6jJgDGy25nY/LT3j4BcDW+gfMzMzCtC9P\nziB3Kul7VaQ2rnyUat8s4emneb321/sH0L3HMOhbhA3j/Lxd4eIe9okZABIRERER0d/GAJCI4kxM\nTgJiq38UOZIqoEhWCDdMw87u8O3uOqSSnitZ9jI4/U41QcSnR+fRv5nqXnyFOs6G409LvHwefCmx\nCwZWVAaAqXHgieaEEmeWq+71V7v3PLjKyZ0n7l84j0kDWspBWJFmA6G87V1goDIs8sCiPtXl/ZWX\nKrceMAFHz1/ABWn/lZMHoW7zoTBx1AwrY12EAaA3ts7oiWoNumDnuXtwEtudLQyxYmQnNOo2DVZa\nTvferrHQU6TFhuua9x90t9JHh3JlMfvAU1EJ69222fL7U7PLKlEJ68fnF7hw9Q7CGYAXxv3DS5Ar\nmR5yFi2PWtWroXLlymqtErKlS4cJO++LvbVTXgKcTxkAlmgU5hJg86cnUSlXcqTPURR1atUIdfzK\nKJwjLZqP2RJyCboaBoBERERERPS3MQAkojgTkwGg0uOTW7H78muxFpaL+StMGdQZNas1xNQ1B+As\nD/n7iaXDe6Jdr9G4ZfBD3s/u02ucOXMOV2/cxM2bN3HpwjmcufkAjmrh09enZzCgQwNUq98cy/Zd\nlS9vtTM4hD79BmLX4fPQ/2AKP/VhfV72OLnjP3Rv2xj16jfBlIXr8MHCRWz8y347AjAIXz++wo3r\nV3H+3EXcf/YODl6RG6Po52aFBzevSu/fGdy8/wy2qoRUK6fvBjgn7Xf+4mX5fb529ZL8uDuPjDWC\nMy97Q4zq3Qadeg1GjYrlseic5v0eNQXC8Ol9XL56XT5meO2R/ketIzLVKUdnPrwj7f/kFdzUXobl\nl8e4ePmq1uP+ag+kx4TzBAwAiYiIiIjob2MASERxJqYDQIqkP7gEOC4Z39qLvoOn4ruHtnF18QcD\nQCIiIiIi+tsYABJRnGEAGEeC7PBvowooWKoyZi1ehmXLlG0NHr7RvNRZVzjbmOGR/if85QulY0yQ\npwUObAt+n5dhcLM6yJO/KE6/i/rMMAwAiYiIiIgoOhgAElGcYQBIFDUMAImIiIiIKDoYABJRnGEA\nSBQ1DACJiIiIiCg6GAASUZxhAEgUNQwAiYiIiIgoOhgAElGcCQ4A+/TpIypE9DsFCxZEmTJlGAAS\nEREREVGkMQAkojhjYmIiB4BsbGxRa6VKlYKXl5f4SSIiIiIiIooYA0Aiojhy+fJlOcwZPHiwqFBk\n3LhxQ37fevXqJSpEREREREQUEQaARERxYObMmXKItXr1alGhqDA3N0eGDBlQuHBhuLq6iioRERER\nERFpwwCQiOgva9eunRz+Xbt2TVQoOpSXwFaoUAGpUqXCx48fRZWIiIiIiIhCYwBIRPSXuLu7o3jx\n4nJg9eXLF1GlP9WjRw85UD1z5oyoEBERERERkToGgEREf4Ey8FMGf8WKFYObm5uoUkxZuXKlHALO\nnj1bVIiIiIiIiCgYA0Aiolh29epVOZxq06aNqFBsuH79uvw+Ky+xji5Ppx+4c+sW7j54hFevX+O1\n1J49fYyHDx9qtEePn0D/1Wt8+GaNIPHYyHL8bogdy6ahWf2ayJgmHY49tRBbiIiIiIiIYgcDQCKi\nWKSc5EMZSk2ZMkVUKDaZmprKk4P88UhLe31Uz5BU/uwWnHqJwMBAjebl+AObxrdD/mYj4SweEjUe\n+Kd5Kun4yXBa30rUiIiIiIiIYgcDQCKiWNK3b185QDpw4ICo0N+gnBykdOnSSJkyJQwNDUU1ijy+\nokeOtPLnt+jMK1EMzQ+f3n+Bs1egWI8KOwxlAEhERERERH8JA0Aiohjm6+uLatWqIUmSJHj27Jmo\n0t/Ws2dPOcA7e/asqESBWgC45NxbUQzmgQun78DRW6wG87HHyn/aI1veUujRvz/6dm2JzMkVyFa0\nOk69tBQ7BQsvAAzExXVjUaJ0FfTq1xcDBw9Es5rlkCZdJiy6ZyL2UbE3vIkuzeqiS//hGNC3D0rm\nSgNFiiwYMn+vdIZEREREREQhGAASEcUgKysrZMuWDVmyZIG1tbWoUlxZsWKFHOLNmzdPVCLJ4yt6\n5sogPzZLrnwoWrTor5ZaT4H8bWbCX+yqEoiZ3crL+6+/ZSxqgK/xZeSVaplK9IGNxkBB7QHgy91T\noUiRG5e+i4Lw4PpebLprINaAfZNaI2elvmItxP2tQ1XnXGkg7PxEkYiIiIiIEj0GgEREMeTJkydy\n+KIc/accBUi6IXhykA4dOohKJKiNAFxxJfRlxG44uvciHLzEami+drhx+iC27j6Oo1sXIHcyBbKX\n6QvrSASAgd6WmN6pivy8ijTZ0WfMXDz5oJkG+lk8Rp4keqjcdCquXb0qTzIT3G7evoenT5/iuf4r\nuPlEdXoSIiIiIiJKqBgAEhHFgL1798qhTf/+/UWFdImZmRnSpUsX+clBInUPQE36l1Yia5qMmH1U\nX1QAJ4PTyC8dI0vpyAWA6uxN3mPl5CHIkFQhn0efBccQINX9vj9B7iQKZCzTFpbhjfIL8IaXX3Tu\nTUhERERERAkRA0Aioj80YcIEOaBZvny5qJAuUk4OUrZsWaRKlQqfP38W1XD4fAsZAXgp5NLb8Lh+\nuoqs0r6K/I3wSe0GfF9vbZLr2cr2gZOyEBSIQHlgniNGtlYFgGff2CoLMoNzp3D5wiexFmJx98rS\nvuXwykG1vmdqW/nc0mQrje0XX0A96vuufwqTZ/0HBx8GgEREREREpMIAkIhizOz+daBQpEPPYeMx\nbdo0uY0b2AZ6ymBEai0Hh9QnjuyLnMpRTLV7w108Pr4JDAxE8+bN5dd26dIlUSVd16NHD/kzO3fu\nnKiEcP7+BmtXrULfNrWRNXt25MiRA3mKV8bEuctwXO0efGEF4OSqYUglvutJ0+RC/9m7YG7xBg2K\n6Mm1ojU745GhJb69vopZY/ugQLYc8vErNe6CZduPwz4Q+H5hK/IkT4XMRcqjU++BGDhwINo1rory\njbrhrZXmyEWrd1fRpmYJ+di/Wqq8WHL0odiDiIiIiIhIhQEgEcUMXxscOX0AlqFufefy8SxKJVdA\nkb4Yjhu7imowd2w8sQdvf8a/kUouLi4oUKAAUqdO/fvRZKRzlKM1lYHZggULRIWIiIiIiCjhYgBI\nRLEq4gBQEuQHo1f3sWfTf+jXvgnGn3wMZ9OXGNSmNgqWqoLpm3Zgz97d2LZzp3yfvXsvTMQDvfDo\n5BFs27YNu6X6wTOX4RRm3g0f3DyyAf26NkORwoVQuFIjzF5/CLbeYnM0ffz4ESlTppRnhFUGgRQ/\nXbt2TQ4BozQ5CBERERERUTzEAJCIYtVvA0Bhx4x2chiTs2pbvLLwlGtfP3+Ag6ty2gPg/vpB8vau\nE/bL6784PkepDNLx89fAe/kmayq3N/2DTIVrwsBZFJT8LNC9bGb5OP9uvyeKUXP+/Hn58cpLf4OC\nOMtqfGdqaiqP4lRODuLuHl8vRiciIiIiIooYA0AiilWRDQD3LhoqB2vtN14QFU23Nw7WGgAG2TxH\n6azKALD6rwDQ6d0Z5Jb2rdP6H8yfP1+tLcDQvp1Qu3ZtdB40Bo5RvPJ46dKl8jlMmjRJVCgh8PT0\nROnSpZEiRQoYGhqKKhERERERUcLBAJCIYlVUA8B268JOzKAUlQDQ8NYOed/esw+pCjGgV69e8jF3\n7dolKpTQdOnSRf6Mz549KypEREREREQJAwNAIopVrobnUVoZAGYojhMmmrOYqtu7aJgcvrTfcF5U\nNN3eOFDe3n3KAVER7J6ieCbp+AVq/LrcN/DnezTOlUTaPyn+WaElzPG1wu4te2DnJ9Yj4OPjgypV\nqsjP/eTJE1GlhGrx4sXyZz179mxRISIiIiIiiv8YABJRrPBwdcQ3009YOriRHKgoW4tRa2Bk9h3O\nbiGzdQT6esLc8Bm6VM8i75OqaHPc0v8MZ3cvsYeKl8VTVM6oPI4eWvYbg+nTJqPf0Gk4c/YYKuZR\n1pPg38U78ejlF/hL+wc6f8XQBiV+PXfSlGmQOmVyeblmrxlwVO70Gz9+/EDmzJmRNWtWWFlZiSol\ndMH3eWzXrp2oEBERERERxW8MAIkoXvF2tcO3bxZyyCfz84Kt/U+xop2nk/Ix3+DgFvnpfx8/fiyH\nQNWqVYO39x9OG0zxzufPn5EqVSoUKVIk3JmelfcLfPv2rVgjIiIiIiLSXQwAiYhC2b17txz+9e7d\nW1QoMXJ1dZVnB06ePLkcCCopg+HGjRvL3w9lq169ulwnIiIiIiLSZQwAiYjUjBs3Tg52li9fLiqU\nmPn7+6N8+fK/Ar/QLUmSJLh9+7bYm4iIiIiISDcxACQikgQFBaFJkyZyqHPp0iVRpcRIeb/HsWPH\nypcAhw78tLUBAwaIRxIREREREekmBoBElOg5OTmhYMGCSJMmDYyMjESVEhs/Pz/MnTtXa8gXUcuQ\nIQMsLCzEUYiIiIiIiHQPA0AiStQ+fvwoB3+FChWCs7OzqFJi9+zZM5QoETKL9O/asmXLxCOJiIiI\niIh0DwNAIkq0Lly4IIc3zZo1ExUiTe7u7hg6dGiYwC90K1WqFHx8fMSjiIiIiIiIdAsDQCJKkAIC\nAsSSdkuWLJGDm4kTJ4oKUcROnz6NrFmzhgn/gtuZM2fEnkRERERERLqFASARJUjz5s2TQ5kcOXJg\n9erV8PDwEFuA3r17y9v27dsnKkSR9+PHDzRv3lwj/FO2zp07iz2IiIiIiIh0CwNAIkpwvLy8UKFC\nhTABTfLkyeURXMrZXd+/fy/2Joq+lStXypOAKL9fyZIlk+8pSUREREREpGsYABJRgnPx4sUw4Z+2\nVrFiRZw4cUI8iv6m+fPny59BypQpkSlTpnjbMmfOjHz58smzSGfMmFF+Tcqatn3ZEnYL/ndFOYEM\nEREREZGuYQBIRAmO8lLM4F/GI9uUAY7y0k76OxYvXiy/75cvXxYVovhtwoQJ8nf65cuXokJERERE\npDsYABJRgvLlyxekTZtWI9yLqDVs2BCmpqbi0fS3BAeAypmYiRKCcePGyd9pBoBEREREpIsYABJR\nghIcLP2uzZkzB0FBQeJR9LcxAKSEhgEgEREREekyBoBElGB4enpqnfwjuOXNmxc3b94Ue1NcYgBI\nCQ0DQCIiIiLSZQwAiSjBCG/yjy5dusDBwUHsRbqAASAlNAwAiYiIiEiXMQAkogSjW7duv0K/LFmy\nYO/evWIL6RpdCQBd7L/hxUt9vP9gCJOvpvL9IE1Nv8Lwwzu8eGEId5+Qy8TtLT7ihf5rGJt8helX\nE3w0eIeXr97AyStA3m5ndBn/DBmMMZNnyq9P2VZtPwgHH3mzpkA3vH/1Aq/eGSNQlNR9fX3t1zEW\nL5yLf4cNxuhNR+EltscvvrA0k95Xcwv4/uFV965O1uIzCmnGpmbw8hc7xCEGgERERESkyxgAUrxW\no0aNX4EPGxtb5JuBgYH4KYobuhIABvOy+YRxbUr9en9G730utoRmi2p5UqFax9kwd9ZMnb7eWyI/\ntseKa6ISlvG97cidvRDWXvssKsDF5UORt0RTvLQIJ95z+4jG2RVI0WYsXEUpJvn7+4mlmGVwYSOq\nVqyKf6fOwaIli9ClYRnV+5umGI49txJ7Rd6jvROQMmkqZMyYUa3lwLxD4X1WfxcDQCIiIiLSZQwA\nKV6rWLEi8ufPj8BAbWNoKDH59u0bHB0dxRqF559//pFDig8fPohK3NC1ADCYw6eLKJFRFQK2nLgd\n6gPW3p5fgyKVmuKJhbeoaAoOALsvuywqmq6tHSRvn7brsaiEWPNPdWlbJhzStxYVNU7v0SimAsAA\nd9w6sxODe7ZBnebtMHXFXli6+IqNMcfyw0u8NbYTayFM72yX34M6gxdrHfkYnhubx6HLuPViTTcx\nACQiIiIiXcYAkOK1SpUqyRM7MPghipzBgwfLIQUDwAgEOmFCq9Ly+WUs1wHvPhtgZMcamLfvodhB\nu4gCQG+z26iYTgFF2vy4auIuqiHe7V2EpNJjCzcYChdR++UPAkBr4+dYOXssGtarg7Z9x+HMnTcI\nE/f522BUt1ooUqRIpFuDtkvCnmckHF8yCF0nroa2q6LD8+zgDOhJ743yvQ1u+Ss0xvYrb8UeuoEB\nIBERERHpMgaAFK8xACSKGgaAkff62AJkSqoMnNJj/TUzUQ1fRAHgu4vr5RArXd7G+OwsimrMb29E\nVmm7olAdPHEIdaO8KASAVh8uonfLyqhUozFW7zoHK+eoRG2xINAL9y4fwdDuTX6FeEWrt8XFVzZi\nh6izN3+JST3rycdSpCqOswa2YkvcYgBIRERERLqMASDFawwAiaKGAWDkuH66jiYN22PviUNoUjCN\nfK5N/t2kcUlwaBEFgD/u7pcDvqR5qsHgpyiq+XR4KVJK2zNWagrz0FcYR2MEoMnbB1g4ti9q162L\nzsOm4frTT2KLFv52mDG8LWrVqhXp1rnferiJh0eFwdWtKJo+ifw+zTqgL6rR4/vjHoqn0UOeyp3g\n9IeTi8QEBoBEREREpMsYAFK8xgCQKGoYAP6OO+YMaoFuc3ZDNbevkjcW9q0pn2+6kk3x0SFki7oI\n7wEYYI8+NbJCoZcJB7QkgEeXjpAf22XRaVFRExP3APS0w5l9/6F74/po0rIV5m89ih8OnmLj32V6\nf5v8WgctCX+ylMhaPDgfSrSZCx2YBJgBIBERERHpNAaAFK8lvAAwAC8v7cXsJavxxV77RAORFuiH\nOye2YM7y7bBw14Vfj0kXMAAM36vTK1C7UTu8tdb+s/fixAJkTa4HRbL0WHD4iaiG+N0kIH7On1Av\nT1IU6zhDVATvL6iZQYEq/VZovzdeTE4Cosbs3R1MGD4YB2LjXnoBvnBwcBIr6rwxvUtF1Oq7FJGK\nH4P84WBtDTctExU/2L8AvUavgpuOzAHFAJCIiIiIdBkDQIrXEmIA+PbGYSxavRFmjlp+442KQD88\nOrcbi9bug41n3AWA769sReMm7XDwxhN8/vwZB5aPRetu42Hi+Ge/tR+Z21n+Zbvx6nOiEiLA4QvG\ntK8qbw9pSVCiVmtcfmMu9goR5PYNU3vWD7W/AoUrN8WxZyZiL00fbu1Bj54DcOrmc/l13TyxBbXL\nFkG/ufuiNMHB38YAUJ0v7p/ehyGdmyJtCj0kTZ4ClZv1wPpjN+Ae6utp+OIK5k3sj9wZ0yFNmjRI\nmTwJUmctipHTluLRG9X97H4XAAZ7c+MQxowej/82rMealUswbc5qGDlE8PMQSwFgbPr84Cia1ywj\n3/cvTcZsqFS9IYb+Mxbr9p8J896q+GFZ34Ly+1ek4SQET5Pi72KBleP7o2DWpNBLnhalqjbAv5Nm\n4cD5h/DSkeAvGANAIiIiItJlDAApXuMlwDos0BVj6+WAInsVfPYSNeHFAdUvyktPvRGVyPOyeY6B\nHZuheYum8jG6bb0qtqh8vL4ZtRp2xzNzzajE4PYOlM6WXH5Ml/mnRBUwf7gP9eu0x4PP9qKiYvjw\nOMrnTi3v32bCflGVBHlgXIv8SJatLWzD3HfMCf2rpYciZSHcNNHNqIYBYOyJbAAYZfEwAEyMGAAS\nERERkS5jAEjxWrwIAL3tcXLHWixYshwLFszG+LFTcOa+6ob8QQF+8PX1RWBQEAJ83fH+yV0c3rUG\nHZv1wqmH1qp9/Dzx8eVDHNuzFb27tse2lz+kqhfObVuAupVKo0arXjhx30jeN5i/pzP0H93A3i3L\n0LF1Kzw00nYpXgh/J0tcOnAAB6LQ7r/VPjIu2JFFPeRfhutP3iMqahzeo2lmBRTJi+O+WeTvQ3Zn\nx3S0HbJAvjfbnrn95eN31QgAA+H+M/yI5NNeVUDTctYJUQmCu4tjuBM7mF5fLe/fYOTuX/uYXd2B\n1FKtQOu50qcQ1ps9c5BE2l6iyb+/RjHpEgaAsefLrTnya2o1+5T8cy03P78IJw4JT2Cgf8gxbF6g\nSgrp56XhMLiI7aR7GAASERERkS5jAEjxmq4HgOdWKkOw5Nj7yE5UVBZ3qyz/ojh9/wvpF331eMAa\nncpmkLZlx+7rlqKm5IbhLZSXxyVH7V6jcPnmKzFBgTdm9qwoH6v3ijNy5ReXD2iSQwGFXnpc+/iX\n359AWwwvlVs+r0l774iimiArDGySTN4+5vBzUYyAjxVGdW2M9ZcNRAHYOquf/HjNADAinhhcJwuK\nNZ8Cr0glMn6Y2LwgctUeDme1Sw397J6jahbpfU2dF1eNPUQ1xMvts+TzylGrAyx18NaLDAD/jsDA\nQFULik78J5EeF3IMUSOdxgCQiIiIiHQZA0CK13Q6AAy0Qo9C6aVfCAvgzAfNUWlvLoyXf1FsNniV\nqARzx8xqBaRtWUIFgH4Y362W/JgFdzRH+73cv1CuVxu5SVSEgO/oWUd5L7t0vw0AA71c8ElfH/pR\naF+tHMSjtQiwRL+iOeXzWnr8kSiq8fuOvrWSStv1MO5IxL8smz88gHZ9x8Mu1P2+ds8fJB+/x24t\nAWNo3t/Rp05xjN94QxR+w98Ww5qXwZCVWmZkVfJ1xNpJfZElU050Hb0Md588h5HRB1w+uQ31iio/\ncwW6L9HNYIsBIFHsYABIRERERLqMASDFa7o+AtD+/WWUz50CWav1hokYRhboaoZOZXOgQouJsPeV\nS2rcML1afumXSG0BYE35l8u5N1SXDwd7vm++XK/+z2ZREfy/oUftyAWACAqEr48PfKLQ/PxVYxDD\ns2Nac/m8uqzVMkmH9SvUTyWdW9KiuBfBJcCvTi+TjxGZVn7gWvEoTTe3z0K34QthF+a91u7R4UXo\n0n8aLKNx/e7ZJT3lc6k5eCX+cA7nWMMAkCh2MAAkIiIiIl3GAJDiNV0PAN2sXmPamBl4ZP4Tbo62\nMDc3h71rRHPEumNWdW0jAP0xoXtt+ZfL8ALAGv9uERVBOQKwrjIci0QAGBt8rNGtTGqkyNcU30JN\naPz62GT5nGcefCIqUXdwyTD5GH3COcbHazsx/N+F0DZO0f2HPs7efgb1QYXGjw5iwKiZsNQSFHrb\nfsDJq4+lTyEcHt/Ru3x2KNIVxZ6HX0VRNzEAjBpvb213eoweL29dnh+a/hQDQCIiIiLSZQwAKV7T\n7QDQHbPbVJJ/IUyaKg3Spk0bqmVGsx4j8eyravZZP28PfH15EkWSq0a09Zp9EA7OnvDz9YLFpzuo\nqryfn1RvPn4TrH66ws/PF07237F8UAO5nixfU+lYNvD2C4SflxsMbu5AFqmu3DZuwyU4u4W9X93f\ncGbVvyhXtyvuvTOHvcMPHFk+HvWa9MNri1DBircDFg1sgSLFKmLD9XeiGL61EzrJr63F2vOiIgny\nxfXds5BBvO7wml7RtrBShpJBAbh/dAmyatlHo+VuBNPggYrSY5ydHGD66TWObF2GVjUroWWfqXhn\nHXNBUWxiABh55/4biWTJkqDX5GOiEl2eWD2gIfSSp8LCQ89ELS4E4c6JbViwcBGWLl2K+XMX4tTd\n92Jb9Cln5m5YrggGzDoqKtp4YmG/RvK/1+qtRIN+MHGNeDSxcgT0mn8bo3CxfvjqJkpqAt1+YN9/\nM9G8TnnkzZcPVRq0wYL/9sPcNdR9A2IZA0AiIiIi0mUMACle0+0A0BcXd67HqXvmYl0LbwsMatYG\nB59pThKSWPm4WOPjJ1MxwQnFBgaAidOHi8uRTHq/V1zWHKF6fnF3KFIVwE3j6M0vfG37VGRKr5rQ\np+uEvaIaSqALpvZtgVm7ruHdu3dqzQA/PSOeKcflyy3UK5EZaaTjp87WFUYat1P1x46xrZGraG3s\nOHVDdcyXD7Fy8iBkTqkK72sOXBHJSX/+HANAIiIiItJlDAApXtPdANAfMzsq7+WXDvO2nIK5Xdhh\nK2bvH2Fcr46Ys+O6xqWoRLGJAWAQPr56ID/vnWcG4V/WLVh8eY8PZjZiLYSd2Ud8/Goh1oDvn/Vx\n78FTOIV7r8lAGL55CzMrLUPYYpnF/e1y+Fe45UBoTkck8fiERkWTQpGmNJ7ah7pWPyIuxujTuiGO\nv7TF+yMT5M+y0zhtAaA3ZnQpg5xFq2PGkvW4+uR9pO+PeXD+APSaexD+dh9QSzq+InMnjQDQ5uMD\n3DMwEWuhOL5D3WzSY5JXx0ubv/MnBQaARERERKTLGABSvKbr9wB0tzHB0V1rMaBTYxQtkB/58uVH\nrebtMXf5euibqC79JfqbEmsAGGD/Ec0LZ0SpznPxK6MLdMKoZnnl82gzfDaOHD4GA3NXeLtY4tH9\na+hYPbe8rfOiM/LuXo6WeHHvJoY2V4b7CjQeOA4rFszH9MUbcejIQXStW1iutxu/A8GDzuy+f8at\nKwdRLlcSaVtyLN3/UWwJjzcenDuOXbt2RbqduHA9glAtAHMH15XPq9NwbZfoemBCy3Ly9vH/3RK1\niBmcXo1GncbARtzS8N5W1f04tQWAFobPcezwYWxauQCdmtVERnGLA0WGfFh9VntQFujwEd1bNsax\np6r7oPqa3Ec15WNCBYAR+fnqOFJLj/l3531RiX0MAImIiIhIlzEApHhN1wNAIl2TOAPAQMzqX01+\nvtVXNO95Z//4JHJL9XQlhiL0hfjnF6qCrY4iAAx2Z69qEpu2M3aKioqX+X1kT6KAonxrGGpMbh2I\nNe2qSo/Rw5LfBoAx7SdGtU0vn+/gTTdFTZ0XZrWuoto+f5eohccNiwd2wKx998S6SkQBoFYeVpjY\nVnV/1IYjVmmMwtQ/tgLN+06Hk1oxqgGgx9frKJ0nNzZfD2d0YCxhAEhEREREuowBIMVrDACJoiax\njgD8dHktkkjPV7r9vF+j85TeHZsnnYce5h1/Liohzi/SHgDe3jNRrreZtlVUVDy+PlAFgBXb4LPG\nnDCBWBfpANAdh1fOx5gxYyLdFvy3De4R3Odu05gOqtcx8oioqAm0QbtauaTtybD4hOYM4+o8vt1F\nzUJ50H7oJMybPQuzZoW0vq1UYV6Jam3l9a2Hzv720mqlhV3LQpGzKT7LYak3VnSrjVK12mLBgnka\nx5/+Tz85pFWkKoGRk6Xa3PUwcdB+rfXxJUPR+d//pHfx72MASERERES6jAEgxWsMAImiJjHfA/Dn\nx+uYNP5fLFy0HMtXrMC69Rtx7bGh2BpW3ASAMc/P4Q2qZFAgQ+VOsAp1OzwPw0somEyBAs2nIKJ5\nwgN8PWFjaQlLLe3Ust7y+9FyyH/yuv1PZ42QVbsgzOxWH/3nnhDrAXC2tQlzbGUze3waFaTjKzK2\nxIPPypotfPw1n+HpyXXoPmAGzLUkfx7OTnDzisL9DaOJASARERER6TIGgBSvMQCMebYmb7Bm2lCU\nyJ9H/mV2z50vYkvC9PzaWVx8oSUECvTAw/OncPz48XDbubuvtY50CgwEvJx/4N6DR/jhEu6sEHEi\nsQaA6/9tID1fBsxauwdHjx4N0x7oG4s9Q2yd2kU+xw6zNQPAS//1kOuNJ28RFZVAi4fIqgwA8zXC\nR42b8gVhatOS8mMWHQh/lF2scjVD50rZUa3fUgRHYQFORmhfIReaj94Qcl9EwcXSGGdOXYCJXUSx\noMq9LUPl19Zx7B5RUfF3eIWONQuiZrshuP7MUJ7d28/HFU9vHMXYfyfg9sfI3QdVeQlwVen4ikwd\nw1wCrH9uPfKmViBD7iKoVKEsSpUqpdGyp02GgUs1P7/YwgCQiIiIiHQZA0CK1xgAxhZvTOyQUf5l\n9shjM1FLWPxtXqNBoZTya6w4cb2ohtg6tjmSZS6ARk2aomlTzda4VkWkTJIGqy5+Fnur+LjawdzC\nGpbvL6BQKuUli2Vx46vGzeDiXKIdAehnjTWr5+Pk9Qd49OiRZntwH0e2LUPRoiWw6e5XBHo44MbV\ny7h15568/d6dm7h8+znsHG3w9N5l3Lytqt+/cxuXL9+Eme1PfH59E1eu3cBD5fEe3sf1q1fw6qst\nPBy+4/rlK7h7X/W8d25dx43bb+EVZ1N/B+L90/vSeV/FZ0tnUQstAA9O/oc2LTpg89mn0iMi5mJt\nIr82Q1NbUVEXCNNPr3Hv7m3cffgcVj+j/vMQ5O2Ct8r39ZkhvNRGMJob6uP23fuqzzCc9lTfKEy4\nGVsYABIRERGRLmMASPEaA8DY4oTR7TIk2ADw1qbJ6D5hDW6c2SS/xvIT1oktKj8+voD+959iLbQA\nTG1RGrUHrRbrWniboWOB1FCkKs0AMBx/MwB8vm8SUqYuiuum4V8G6vntOQb0G4QnppGcZpYoFAaA\nRERERKTLGABSvKY7AaAvtk3ujKLlGmL+iuVYsWop+sgzaybBugshl5f6OhlheIvyyFS4BuauWIHl\nc8ehaEYFFOnzYPNlcWmgpz0uHdmEFtUKyr9MZik8GuYWBpjcpzny5soBPammrFfrNQ/efj64sn02\nKpcphKxZ0sp1RaoC2HNPddmum+0XbF42BaVyJZO3dRxyBDdPrESFEvmRJbPYX5EK/RYeDnUpawQB\noK8dFvRtiTqte+O/1aswa2w/ZJT2S5unLHbdU7u/mZ8dZvash9INumLF8uVYuXw+2lSVXpNeWpz5\nFFHI4o313VuiUKFCkW4VWvWBWWRyNh9L/Nu+JdZdVp3n/YOL5NcYOgCMyIOd45C5QAN89REFbRgA\n/tZfHQHo64Tj66eiQn7VbLjqLUPmHGjQrjcuvPgqdiaKHgaARERERKTLGABSvKYrAaD+qVHSL37p\ncPiFg6iomNy5iMs334q1ACzrUFb+BfG/u99FDfAxu4RMUi1LjU6wVru8zfbxMWST6qnzdYChq+YN\n75cMryMfp+vCY6Ki4vv1BookVyBl3i4wVwuoHi8fK+9fsf1caMxNIFk7tJG8rUKvxWqXymkPAN0/\nX0TOtBmw5WnoSwdt0DavKlAZt1c1m+rBuX2gSF8cj+00z/3Fjd249sFSrP095ne3oWWHoTBXy+Pu\nHVKFUJENAH3MbqJQtuzYfO+HqISDAeBvxcUkIESxiQEgEREREekyBoAUr+nMCMBAT2yZ2l3+5U/Z\nqrXojoMXHoR77yy3b2+xe/s6bN17ClePrEHmpApkr90NNmoBoOPTk8gvHStzyVGwE7VgayaqJifo\nt++uqKh4m91GuTQKpMjTDiZqudP9JcqAUoE2w4+KSohA26eonl0674xF8Mo6OKzTHgDunlINijR5\nseXWGzlACmmG+G5pA1tbWzi7itkPPK0xp4cqqFQokqNRt2E4/+CNaluEvLG2S1Pky5cv0q1ss54w\nDW+ugiAPbJrSB2PWXJRnJvXz8/vVbu1fIJ9fuXFrVDX/UFOkavDAiLoF0HzyIbEeAQaAv8UAkBIa\nBoBEREREpMsYAFK8pov3AAz0/Imzu1agfF7V5Ya1B6+At5yrBeLi+tFIqsiAPQ9DRsA5GpyWRwBm\nq9U1ygFg/xgIAOH8CnXzK6CXow3Mfg0P1B4A7p3fWK79u/GWqIQivU436bMIHXx6OVlg35opKKS8\n3Fl6fN9lJ347sUBMcbd4iyH9e6F7t67o0qWLRqtfrYx8PhmKVZDXhy7YjfByxMurBiFPmY6wj8yJ\nMwD8LQaAlNAwACQiIiIiXcYAkOI1XQkA393cif9OPhFrIbYNKQZF+mJ44wUEGV9EEemXw6RZauKV\n2vwST47MRyqpnql2DyjvjBfk4y7fj8/5+SnkkupZS49D6AtuN0zrIf+iOfjQY1FRCbB8iIrKADBv\ne3xTm+/g/pJ/5f1r9lomKiHubR4pbUuKjbdNREXJHRM6qWYBPvrcQtQAP5BqraUAAE7ESURBVJtX\nqJM3qVwv3rQ/7n4MuZTZw8YIc0aNxOmXqv2v7duKozeM5GV1/RpmgyJvF1hp3nQwTkT2EmBXgxPI\nm70ATr4Pb9bUUAJ+oGvhNFCkKIVbEd4s8O9jABiPBAXB7vsnXL90DJOH9sPy7TfFBtJFDACJiIiI\nSJcxAKR4TVcCQNOHO1CiRAkUKVEWtes2QN26dVGzSnm0Gjgd312Dh4wF4e6BJciTPon8S2KStAUw\na8sVuHtZolMJ1Wi7qm3/xWdrF1zbswl9OzZD9erVUa1aDfT9ZzIuPX4PK8OHWDh9HJrWriZvq92w\nFSbPWYdP5ta4dXg7hvZqi6pSvXq1qugy8F+cvqm6H9+DpaoRgHWbjcLI4Z3QpEkj6Rxro0Lpoug+\nZjkcQm7+B/NXVzBt7CDUqiIdRzpWozbdMfe/PWqvIxA39y1BlaJ55WPKLUUGdB2zFs6/jhOE+/uX\noVjRkihZujzqNWgoPV8DVK1QGn0nrYNr+JOx/lWvru6RX2PvtVpGRv7ijsVD2mH63vtiPQJ+Tji7\ndiUmjuiDWsrPoXo1tOkxEAuWbsRHG90YCcgAMP4IClJdkn96Tkf5vRo477S8nhA4//iEmzevYPm4\n9qp/QzJWx3Pb4FsQhMP5I6pnEv/mqLWMlfvCxkf9sT7YPbVrmP2UrXLrIfhoHzuhPANAIiIiItJl\nDAApXtPFS4B1UfAlwK21XQJMiQoDwPjn4nLV/UUTUgD4i+N7NMyggCJnXeiHmjBIna/tc/To0gOf\n1f5YoU2ghzmGd++AvXdUM6GHCMT5NUORMon0XGnK4cbX0NMh/TkGgERERESkyxgAUrzGADByjkzp\nJv9iWrfXelGhxCpRBIA+Vvhv4gBULVMY+QoWQ7M2nTF40CDMXnAJ6tO8fH91DQM6tkLZ0oWQL28O\nZM9fEv3HL4aJnUiZgvxh8fklNi6di74dGqNUg7awCAK+PT2FDnVUM3rnLFULmy7oq/b3tsHaKf2Q\nJ5NylG9qtBg4E8ZOquGuAb6eeHv/AuZNGYfWtcug7cQ1UtEZayf2RrYUytFpKdGo03Dc/xj6jp+/\nCQA9LbF+9mi0aloD+fLkQIZsBdFzzCIY2mmOcrM1vIvBbRqhROF8KFyuCrr17of+XUfg5I1vYg/t\nPlw/jIEDB0ahDcEzE7V7HPyOw1vUSx9xAOhlo4+q2VQj+OSWMgta9ByLm29Dbk8QLDAgolGEPzEy\nb2rpuWrgrWNEE/5EDwNAIiIiItJlDAApXmMAGDFvN1sYGhrBwsoGDg4OsLO2hPFnQ9i56NbEFPT3\nJPQAMOCnPkqnSoFpewxFReXLxZ0YNWL3r8lnds9sJD//zD23RQXwNb6FgsqAqUAHjUl0/L6dQf7U\nCiTJWBgr9t2G069szQeT21aUj1Ot8zDcff1Z1AHHt0eRM6UCyQrVg7HasT5eUM08nbl4Xey58U5U\nAX8HQ3Sukl3e1nbyHnnG6mDhBYB7J7dGzkrd5HuHhnBBvzr55P2Hr78kVz6cmYnkiuJ46yKv/rJr\n3ERsPBq334PIBIDq/L3c8OTqEbSrU1x+jYq0BXDk2Q+xNWIvD0yCInVe3PjiLioxiwEgEREREeky\nBoAUrzEAJIqaBD8CMMARk7qoRucVqtEOm0/ehou2e04G+MDKRvXvhp+zFe7euIojG+agYFIFkmZs\nh09O8iaZv8VDFE0v1Ys1gHGo7Oj6un7yc/WeHuryemcjVMyVDIocFXDHJmTGm3fnl8r71x66KMxM\n2EEO+iibLSkUqYrj0peQ+ai1BYBWT7YiXTIFmvUZjaVLl2q0GeNGy5/z6KX74S3t6/n9LsrII+iS\noFX/8bj5PCSo/B1z/TtYs2ZNFNpafLbSjCQjFMUAUF2gwxvUyCu9x1kq4Inae6zN0fndUbfLJDhF\nZhbvaGIASERERES6jAEgxWsMAImiJtHcAzDAHXdObkb7WiXk51EokmPIiqMIzgKDPH9garfaqNV+\nLEwdxZC+H89QW7lvhrYaAaCfxQNVAFi0PozcRFG49l9f+fi9ph0RFcH5868A8K6WALDm4AUalyPL\nAq3Rv0RmKJIXwgW1pFFbAPj5ouo4LaYdE5Xf83Iwx/blk1E6X1r5scmylsbBh+qzf8eBPwgAlZ5u\nnYTk6UrisZ32S3qtXp5C1w698eBrFELJaGIASERERES6jAEgxWu6HgB+M/4ASztnsUYU9xJ6AOhl\na4xXhm/EWojzi/vLz7fwwmfA1xTllbPJ5q6KT+pzQdjqo6a0jyJDe3xWu1zW/8cjFMugCgDDjABc\nGzwCMFQQ5yJGAOasiHu2IeFUcABYdegiUQnh++0msiVToEr/tRqjAy+t7CE/ZvD8s6ICBDp/QqP8\nyaV6EnSetgOh58bw/fES/23eA3c/wNLwPowdQv8b6YKhVXNCka4Anoe97eDfExwA5qqHV/aiFlne\nZhjUtSX2Pgt7H0M300f4t+8AXP+gJfjztMLObRvw3jZmb4XAAJCIiIiIdBkDQIrXdDEAdLL8ivvX\njqNxYeUv5wosP/ZIbEk4ji+uKb+2JecM4ObmJjcPL81JB95d340GZQvJ+ylbwYq1sWTXZY17m6m7\nc2gN6lUp+Wv//CWrYdTctfjhof0RXlZvMX3kYCxYsRH79u3DgYO7MXv0YMxZf+zXKK8/4e/thg/v\nXmDrkhlo36IucmWohGMPo5ZQHJ7XUXot6bHrhrWoqAvC9b1LUb5wtl+vuUydFth9SUwo8UsQvDzd\nf73PdzYOkfftfeCx2B41CX4EoNsrlM+qQJp89bHp0CW8ffsWDy8dQLV8mdFo5Hp4yMmaD1YNqKV6\n39PkRJv27dGx+3Bs3b0V9fOoPotqjVpj7bErMDZ5hQ0T26v2VSTFuNWHYWBqAw9Xezy+fR5ty2WV\nt6UoUhNHrzzEdyc3WJu9xeHlY8RjFOgxZSPefrGUT++9CAAzZ86K1r3H48J9fekc32DHomFInyo1\nZmy7I++nFODuiDfSczQrklF+TPIitXH82kOYO4nLg73sMa1/vV/Po956zNn/KxT8fH2aXKvebiwu\n3HsuvydH1o5F1vQ5sOTUa7HXX+bngU/PnuLImvFIKs550JwtePziHZy91eJPfwdM7VZGvK50qNuy\nE0aMmYTJcxbh3JP3YqcQFq9Po3JO5SQsmu9H6LbqspF4RMxhAEhEREREuowBIMVrujwCcPWYUvIv\ngwkxADy7upn02pJj052wI2887Y2wZPpcPPyqPvLRH9smd5Hfj4Ltp8r3JQvmYXEbtUoVwqQNJ2Dl\n7AU/Pz84WRph8/SBSC9+Wf9341Wxt8rNDcoQKz8ehZ0EFHtG14YiZVG8+xlTN/tyxZjSmaXnK4CT\nTyP3PfOxfINuLSqgSGZlcJMX+29rBoD2Xx5h1vTlMFY/xwAnTO1STX69jSfuEsWw9JUTGUj7DDv2\nXFSiJqEHgEGB6oFxELy9vOAbwVdB+X0LLSDCmWT/zK9LgIcuURUCfeElneOfC9L6WpQ03hE/5fOp\n/wRSTGEASERERES6jAEgxWvRCgAD/WBtZYWfzq7w8PCAh7srrH9Yws075D5dzj9tYWPvCDdXJ9jY\nOWjcq8v0/SOsWjAZEyZMwtrdJ2FqG+qmYMLq0ZoBoLe7E759t4Cji5v8vD5+4vmC/OHwwwLWdj/l\nuqf0y7m2vMLP+RuO7VqDCRMnYenWozC2jsxMlkHw8nCFi4tLpJuru5dGYKBNRAFgeNz0TyOLIilm\nagRX/jB49xnhTUpscG2D/B5WGL5E4z25vUE1uqpYs1GwUh946PMdTfKkRNkuCxEyhcKfssM/JSMf\nAN7aNgUthi6B8rRmVcsrPS5bmAAwPEaHlaFYRux/photpg0DwPjN8IoqAKw1cpmoUELBAJCIiIiI\ndBkDQIrX/mQE4KN9U6En/bKmyFEOnzWvXpVdWj4CvSZvFWvA48Oz5V/ueiw4LCrAs32z5FrWpqPh\nHCo1Cx0ABts6ooFc7z3juKio2D3fjQzK8ylSH0Zq6dWPJ/tRKE8u/Hc55HI3kzvbkFe5b4pCOPnO\nRlS18YeNhTGMjIwi3b58twk7OUEoUQ0A9c9vQK06DXD9S9Tuh7hleA3o5ayBj05hI8kfb86iRv50\n8nuZr1IbjBg1HBMWb4dt6Juh/bFIBoBelhjRuTn+uxgcrDljcuU80uMiEwB648LWyajeuKvGvee0\nYQAYP7l8e4d1i+dj9PDhGC61IYOGYcqsBbh4J44n4aAYwwCQiIiIiHQZA0CK1/70EuDbW/6Vf2Gr\nN3ydxqg307s7MWTyankUVzDTpycxdNg4vLYU1QAfGF7dgWLS45Nk6gSjUPeaDy8AvLSyp1zvP+eU\nqKj4fLuLgmkUSFGiEYxFAOjx5RoKSPu2G7wCx48dw7Ff7QT6tiwtH6dQjS7429OMRC4A9IfJi6e4\nc+085k4aiqI508jnq5etLG6b/H704rYxbVC+wySEn4e548DqqRg/fyGmD2glH1uhSIXB8/chJi6o\nDPH7APDr3b1o0XEkzDRGMrr+CgAP3AknpPX3hMHD+7h+7igmDOuKnOmUr0GBLOXa4bOz9hiWASCR\nbmIASERERES6jAEgxWsxcQ/AraMbyb+09VysCuR8fzzGiJGTEJzzqfvx6jwG9uiCZdtO4Yern1R4\nrpo1NHPsBICvL/wn7ztnR3R/ofTG68cXcPbs2Ui3yw/ewOc31wBH5xJgpdvbxsqvp+Eocf8zLZw/\n30HHNh1w4W34Ixv1j82RjqOHrfetREUpECeWD0MK5eeRoRhuftUy+2e0RBwAXpjRE0lSZkLt+nVR\no0YNtVYVudIoJ4JJhiKlKkvrtbH9QthJC0LbOrGN/B4NWXtaVDQxACTSTQwAiYiIiEiXMQCkeC1G\nJgEJcsPYZgWlX9ySY/mB05g3/l88MAt1BzlfW/QqmwaKbBXxWW14WaD5ExEAdg43AFwRKgC8sLy7\nXB80/5yoCDZPUDitZgDo8v48CulJx09fHJc+OamKGnxw7/I5zfvg/QXRDQBdXp9EBkUK/Hftq6iE\nCHQwxKTB/bH/7hdRUecFo09Gqtl9gxwxuLr0nhRri69aLve1fLRbnlV07IZ7ovKnggPAgjj5VNtn\nEB4vTKuivAdgdhx+8FPUfu/DoflQ6OXC5dBfKIEBIJFuYgBIRERERLqMASDFazESACp5f0P7Usr7\nyWXG0XdajuVpiuZFVZewtv53NUy/meP6iT0YM7A9cidVIEm60jhy7RZOXLiAn3JK5Y8pbZWXfyrQ\nf+VJ+RDBvt3fjlRSPUWeyjjx6AO+mRli34Z1WDZvKvJkUEAvaxlcfm8BewdX+bJkkzu7kDuJQj5W\nkkwF0bX/SIz6ZyR6ta2LYpVbQt86VFj5F0QUAL44OA/JpXOt2Kgbdpy6CZMvxvj0+gEW/tsXzbqN\nxFfnkMlWlFwtXqN/g8Ly60uaPJn8/2FaqpJ4YBmS9vk7f0O/uoVRpMFAfLINef1O5q8xpEMjjF13\nUVSUAmGsfxGTxozG1hMPfzvBSVhW6J5P+dknw/oL2sLJ8LhgfNms0uPSY89N9ZGKwPE5vaV6cjTo\nOhLHbzzFF2MjvH54CeN7tUPXfxfCMYKbMDIAjD1+7na4vG8tmlTIJ59bpXYjVKFzAuL48QByp0mK\nYo0HYvv27ap28ASsPcJOPfTmyl6MHj1ao40aMwMvLcJewv/T9A22LpuNYcNHYP667Xj/PaZvTOCF\nVw8uY/f6/7Bs8QJs2H0UVu6a/5Zo8sGbR7ewa5v0b+uShVi/7RC+OYX+i4EvHl848et9mDemM5Im\nTY6xyx6I7VHDAJCIiIiIdBkDQIrXYiwAlDibfcCDJ4ZiTbtXt45j3tw5OHTxEYJ/9fz06AxWrtyI\njz9UvxT7ezrD3Owbfjq5wNXVFY72tjAz/QZ3H7VUJ8AZZ/evw5x5i3HrpWoSgCCXr9i+axeeGZjD\nzcNbrqlztzbByV0bMH/+fOw8eRW24cyc+zdEZgSgh5srnJ0c4eIewYkG+sHJzh4uLq7yexVe8/EP\nPxHzcXPA169f8dX8B7y0TZ8cFIB3d09h6Zr1WDtrAFp2mqpxb8fwBcDe0gxm337ASTk7snQe9jaW\n0nOZw9UzouAhWBC8Pdykx7nBz19b7BgAd1cXOEnfXTfPsJ93eBgAxj7jW/Pkc6vdY+Kvn/OEwvHD\nPmTSk17b6L2iot3N7VNRpWEP+d8b9Xb6kea/kTZvLqNXt544cfs9nJ1dYG3yGtO615LfvzTFm8PA\n6Q/fwUA3TOtSHmVbTobmT4k7BtfIhtR564X6I4gXlgyshYINh0FzfnYfTGqeD3o5KuCBufaZ2z9c\nXiaf96gl90UlahgAEhEREZEuYwBI8VpMBoAUecEB4JZ7FqKi+748P4uhw//BCy2jl+KT14cmyyED\nA8Do8/PzQ0AEQ0E/XlPN+B3TAWCAvx/8tYXUf1FkAsDbm8eidPXWuPrsLVy0pupCgDcc3bWNQA7E\nnFZZoEhXDs//6P4EgdjQt7r0WaTCFn1bUQvhb/MExTMqoMhYFc+tVX8k2De2vrR/cqy//V1e1+D0\nDlUyJYUiTUnc/hr2DxMMAImIiIgoIWMASPEaA8C48eLycnTv3gPjp8/FokWL5Lbr7G1EcOVqnAny\n84LVD0vEce4SfUEeOHtww6/3eeakf+X3fueTqFyOHCK+B4Dmxq+xbHRb+bHK1nHCarz7GjJhjPtP\nS1zaPQ+ZUqTG+M2nYefuB/h7YP2wlkiSOiMW7z2P58+fY8uMPvLj81bpg6+emt9c9QBQ3uLjjBdP\n7mHewCZyPW2hLrASAaKd8Qec2b0YecT5rLlupNogPDg4GyVKVcaqvZfw7NlzbJszSL5HZYYidXHd\nMOJ7Sj45MwdNmjSJfGvVF7eMI7709ncBoPX7y+jTsQNaNq6L7BmSyq9J2fKUb4XLb6zFXhHTP7Ue\nI6ZtjuRI24h4Y0KzMtLz62HOXm2hmgP+La28z6YCy48/kyuLO1SU16esvy2va3LF1Faqy7sn7b0j\naiEYABIRERFRQsYAkOI1BoBEUZNQRgC+O7lQfnyJ9jOgNi+PxA/rpgzDjlshE83YvD2INHoKNBy0\nUiOkXtJYOVFPaqw7qTkpTXgjAJ0MTiG/VM9Wtj9sNEYQemNi13LyYzbfUV3Sr7RpeB3kKN0focfI\nGV5RvXaFohAemkf+8u+YENlLgNU5fXmI9hXzy+fcYNgGrWG6xZtrmDiqP4rlSCVemwJpitTFnS9R\nmTgnLE/L56hfMAUUSfLijIHmsdxMbqBQVj0o9HLi0CNVCOxna4hmJVT3cz30XDOw9LR4iupZleeX\nHrvUvh/BGAASERERUULGAJDiNQaARFGTkC4BPrO4l3yM5uN2iApwaeMUzN1xQ6xp8rH7iHVLF2PN\nht14+c4AsxuVlh6fFhvOmIo9VMILAB3fnUQ+qR42APTChC7KkWpqAaDzRxTLmAwl686Du7cXvLxC\nmq/We0KGdfvAKJQsWTLyrXxTXPgQ8b+F0QkAg20YVAWKNPlx4dvvQ8sXx+YitfQ8yQrXwjuXyL3e\nCPl74NmNMzh0+Agu3X4AK2cnbBvfWn7Pm4zeEnZynyAvvLh1CQcPHcbF6/fxzdEZh2Z3k/ev2X+J\n1sldGAASERERUULGAJDiNQaARFGTsO4B6IclvSvLx5l88BWsnh3A0Clrw4Q7Fk8PIlcyBdrNUp+R\n2weLGpaUHpsmGgHgANiGHgHYraz8mF8BoKcZmudPDUXq3DjzwUVVC8XH8Tu+O2qfkCK2/EkAeGrV\nYOSt9w88IpXnBWJmo9xIU6od7GLyRooyLyzoUUV+v0f/d1XUIhKENcOU9wZUoP+y06IWFgNAIiIi\nIkrIGABSvFaxYkXkz58fgYHx9g5vRH/VP//8I4cUCWYSkCBnjKhfQD5WyTZTQl0OrOSPqb1UYVGn\nmQdFTTkpzBnUy5NZqqfGlssWQKALrOxVYdzX2wvk/av2mCSvB/O3uo/SGRRIkrkMHlkHp2C+eH5u\nB6oVSS89Jim2Pf4h6tJz3NqKDNJxlMeq0XYErj//DCcnJzjZf8eBZaMxaOpauP7lG2dGGAAGuuHY\nhnlYsuEQrNxD/k0N8LTB9sXjMGXFUenVhgjydcbRrcuw4dAlOPuopYIBrtgwriOqd/wHNmE/ELi7\nOsM3UiFiaAE4t+5f6T1Njb5z9+D3E6EH4dq2aciSUg8dJ29AxHdHZABIRERERAkbA0CK16pVqyb/\nwsXGxha1ZmBgIH6K4kaMBYCSAKtn6NahJ/TtwkmVfKyxelxHZE6pgF6a7Bg0exc8A4F35xcgV/rU\nKFu7E669Vc4y64LTu5Zj8oRxGD9+PMaMHoOZ8xbh3BNj1XEkDp/voVcz1UQTmQqUw6L9qskmZgyq\nJx2nNSbNWop7r77A2y/4XPxwQTpm8xqlkUx6TPKMudGyz3g8+/pn98aLrt+NAPSyNcH+TcsxctgQ\nDB02DjuOX8R3W1exNbQgmH98jLULp2GEtP/AYSOwbNMuvPhoJraH5ov/RrdBslRpUSBXDjQevgy/\nu5jY7O05rFi+FCvXbsLFO0/hpu3aXTUWH+7K+y9fswmnbzyGSxRuscgAkIiIiIgSMgaARET018Vk\nAEiR9yeXAMekY0tGYuK602Hv3ReHGAASERERUULGAJCIiP46BoBx4+f7XUgtve/5m4zE2bNnVe3q\nLfz0+gtRXIA77t+6ggu39CNx+e7f4I/3D2/9eh/WTu8ufyeHzr8jtkcNA0AiIiIi0mUMAImI6K9j\nAEgJDQNAIiIiItJlDACJiOivYwBICQ0DQCIiIiLSZQwAiYjor2MASAkNA0AiIiIi0mUMAImI6K8L\nDgAvXbokKkTx24QJExgAEhEREZHOYgBIRER/3fz58+WwJF26dMiZM2e8bbly5ULRokVRvHhx5MmT\nR+s+bNpbjhw5kD9/fvm9K1SokNZ94lNLkiSJ/J1+9uyZ+JYTEREREekOBoBERBQnAgMDERQUFG+b\n0p07d5AiRQrUqlULrq6uck3bvmxhm9KbN2+QKlUqlCxZEk5OTnJN277xqRERERER6SIGgERERNGw\nevVqecTX6NGjRYWiw9PTE2XKlEHSpEnx4sULUSUiIiIiopjEAJCIiCiKunbtKod/Bw4cEBX6U716\n9ZLf0x07dogKERERERHFFAaAREREkaS8zLdEiRJImTIl3r9/L6oUU9atWyeHgIMGDRIVIiIiIiKK\nCQwAiYiIIuHDhw/y/eqKFSsGFxcXUaWY9uDBAzkErFSpEry8vESViIiIiIj+BANAIiKi3zhy5Igc\nSnXu3FlUKDbZ2NjIM+tmyJABJiYmokpERERERNHFAJCIiCgC48ePl8O/lStXigr9DcoZdRs2bCi/\n92fPnhVVIiIiIiKKDgaAREREWgQEBKBOnTpyAHX79m1Rpb9typQp8mcwc+ZMUSEiIiIioqhiAEhE\nRBSKtbU1smXLhuzZs+PHjx+iSnHlxIkTcgjYokULUSEiIiIioqhgAEhERKTm3r17ctikHP3n5+cn\nqhTXPn/+jDRp0iB//vxwcnISVSIiIiIiigwGgERERMKqVavk8G/cuHGiQrrE09MTZcuWRdKkSaGv\nry+qRERERET0OwwAiYiIJN26dZPDv8OHD4sK6ap+/frJn9WePXtEhYiIiIiIIsIAkIiIEjU3NzeU\nKFECyZIlw9u3b0WVdN3atWvlEHDYsGGiQkRERERE4WEASEREiZaBgQGSJ0+OYsWKwdXVVVQpvnj0\n6JEcAlatWhW+vr6iSkREREREoTEAJCKiROnAgQNyeNS1a1dRofjIzs4OefPmRdq0aWFmZiaqRERE\nRESkjgEgERElOqNHj5bDv5UrV4oKxWcBAQFo1qyZ/JmeP39eVImIiIiIKBgDQCIiSjSUl4nWrFlT\nDoru3r0rqpRQzJgxQ/5sZ86cKSpERERERKTEAJCIiBIFc3NzZM6cGdmyZYONjY2oUkKjHAGoDAGV\nIwKDgoJElYiIiIgocWMASERECd6VK1fkUKhBgwYIDAwUVUqoTE1NkS5dOuTJkwe2traiSkRERESU\neDEAJCKiBG3u3Lly+Ddt2jRRocTAx8cHlStXlj975WzBRERERESJGQNAIiJKsFq2bCkHQKdPnxYV\nSmyGDh0qfwfWrVsnKkREREREiQ8DQCIiipcuXryIUaNGwc7OTlRCODg4IG/evEifPj2+fPkiqpRY\n7d69Ww4Be/bsKSqalCMEBwwYAAsLC1EhIiIiIkpYGAASEVG81Lx5cznUCW4dOnTA58+f8ezZM+jp\n6aFixYrw8vISe1Ni9+bNG6RIkQKlSpXCz58/cfXqVVSqVEnjO7R69WqxNxERERFRwsIAkIiI4p0H\nDx4gWbJkGuGNesuePbs88QdRMG9vb8yfP1/r9yW4lSlTBv7+/uIRREREREQJBwNAIiKKd/755x+t\nAY62pgwDlfd/c3d3F4+mxOLbt2/4999/kSRJEq3fDW3t6NGj4tFERERERAkHA0AiIopXzM3NkSFD\nBq3hTXgtT548OHnyJAIDA8VRKKFzc3OTZ4BOnjy51u9EeK1Vq1biCERERERECQcDQCIiileWLFmi\nNbjR1vr06QNnZ2fxSEqsXr16hSpVqmj9joRuSZMmxZMnT8QjiYiIiIgSBgaAREQUb3h6eqJIkSJa\ng5vgliVLFpw4cUI8giiEn58fZsyYofV7o96GDx8uHkFERERElDAwACQionjj0KFDWgMbZWvXrh2s\nrKzEnkQRU47yK168uNbvUtq0afH9+3exJxERERFR/McAkIiI4oWgoCA0aNAgTFCzc+dOsQdR1Pn4\n+GDUqFEa3ytlW7x4sdiDiIiIiCj+YwBIRAmC8j5vU6ZMwcCBA9GvXz+2BNYGDBiAtm3bagQ0TZo0\nkWd47d+/v9bHxOfWt29f+XUpX/fu3bvFtzxueHl5yZfNJuSfLeX7rPwulShR4tf3K1OmTPI9JBPi\n94tN9Zlv3LhRfMuJiIiIEj4GgESUIFhaWiJz5syoWrUqvn79ChMTE7YE1IyNjdGlSxfMnDkTnz9/\nTvCfsfL13bhxQw6ilGFgXHJ1dUWOHDlQunRpreea0JryvX/79i06d+6MOXPm8N+TBNaUn+fdu3eR\nIkUKzvhMREREiQoDQCJKEJQBYMaMGeVLRIkSAmXQqQwAlaPQ4pIyAMyWLZs8iy5RQmBqaopUqVKh\nZcuWokJERESU8DEAJKIEgQEgJTQMAIliBwNAIiIiSowYABJRgsAAkBIaBoBEsYMBIBERESVGDACJ\nKEFgAEgJDQNAotjBAJCIiIgSIwaARJQgMACkhIYBIFHsYABIREREiREDQCJKEBJkAOhqgk5lk0Gh\nyIq9r6xFMXr8rN+gak4FFElL4I6hi6jGgQAnnNu7FYuXLsaCxQuwbMMuGNv7iI3R9/XeVtSqVBOH\nbluKiiYns7fYumollixbigULFmD50mXYdPACnAPEDjqIAWDk+dm+Rv3cyu93MVwwdhbV6HE1uYvC\nqaRjpa6N1xZ//t2MNi8bHNu+AYuWLcJi6Wdl5daD+O4aJDZG3qeHZzC8ZwsUKVwUlZt0xard5+Hs\nJzZqYW34DBtXLMdi+WdlIZYtW4btx2/AU2xXZ3J3J6pUqoiKFcO2SpUqoVKFilh18pnYW/C2w4kd\na7Fi2XL5Z3HZ8mVY/d9WfLRyFzvEPgaARERElBgxACSiBIEjAHVdIPZNbQFF8hJ4YS9KMld0K58S\nJZpOQjSyDYknlg1vgOSKJFAocmDTOTNRFwKcMKJ2LpRtN0cUQvh/PY9sCgXG7XgoKrqFAWBi5YfV\ng6pBkaUWvnqJkpKfJRrmU6B6rxXwFaWIWH+6gVEjxuHBF1tRCcLTM6tQIKWe9L3KgL33NH9Wgnys\n0LlkOtQfslFUQjjr70RK6bu49MwHUQHcvtxCg3LlMXf9Ady+fVuz3bmHab1qI2ulnnAIFA+QnFvY\nBXrJauJrmADSDd2KJUO59rPhLSqxiQEgERERJUYMAIkoQYhPAWCQ90+8evoQ9x8+g43b74agBcLC\n7CtcvPzFerAAWJh+h7Nn8OO9YfzhI8xtXMW6Nt4wNTeHu6/ab+R/yemFXeUwa/iK06ISwuzmaqSQ\ntpXrMi9SwUYwu3dn0ahJJ3x09MK6FmWk46cNEwAG2L9DrTQKJM1WDrdMPERVxfbxLjnUmLLviajo\nFgaAgJ+7DV48uo8HT/TxUz0M08oX381MtXy/ffDd1EKqi9UAd3yWflZ+OGgb0yYEuks/K98Q5sfu\nL9g+rqH8uc/YdVdUQrw7MV3e1mjkBkSclwchIJwf84PzWkOhlwfXPjuJioqn6T2UTKJA6oJ18cpG\n88HGF5bKz7vy8idR8YeDQ/gjLZ1f7Ue2LHlwykB9tLEPprStIB0nKWbtDzUq0M8UdTMrUKHnYmmv\n2McAkIiIiBIjBoBElCDEhwDw5bF5SJssJf67ZiIqwKtjC6An/WKdNn9lLNu8AwdOX4Uy5zB7+wSH\n1k1EOmmbIkVuXDJShleB+PzuCfb/NxnJlXVFBaxavw0TxwzD4mUrMH98X6RS1tOWxt1v4nK6QF98\neHUX2xaMkH+BV2SthSdWEaca9l/vYunChVgY6bYUtw0iuETZ5S3qlUwqPX9GrL9uKoohfC0eo6D0\ny78iSWlc/hC5ywD3LxiA3tO2S++IkjcW1C8uHT9sAKjkafsJXesUlF9/ukK1sO/kWSwf2wd9x6+E\nbRxe4fk7iTkAvLXlXyRPmQkHXwSPXgNubxkrvx9ZSzXA2m07cPTSXSgHkhm/fICdi4cgqfz9LoXH\n1spozBcGrx5g64Lh8mMUKetjw5bNGPPPMCxbvhIzhneS68ly1cWbnyIZ9PfA22c3sWZKT9VjirSB\nYUR5usTiw2UtPw8RtVV49tVRPDosf6t7KKO8VF9RQHrtdqIa4ufHS8iYTNqeqg6efI/69evPDi9G\n37HLEN6/AE7fnqFphezy689WpgWOnj6DuUO7Ytjs7XCJ7AjdADt0Kp0F/VdfEwU1gW7YML6l6v1N\nmh1T1h7BsW3z0arTELz4+meXbkcFA0AiIiJKjBgAElGCoOsBoMurMygg/dKboegoaFwBiyAsG1ZF\n/oV4zrFQI9H8vqNtlQxQJMmBS5/dRFHi+x398qSUHlMKNz9rXkt3ZEpz+VjjVt8QFcHxIxplkn7p\nzlLjtwFgTPMwuowKqZWhRQEcNNQcdaQUYK2PwtmUlyVmxoEH30VVu8Cfn9C9TWuc0FcPHCMOAFU8\nsXvRWDRpUEce9adQpEL7f5fD5m9cbxhNiTUA/HFjB9JLr7tAtTnQHLPpgQmdCsvvydobhqImuHxA\nzULSdyhTCTy2UgvGHD+gdVrp89arA30bURM2Dlb93C068FJUVHy/PUGFpNJjirT+bQAY0+xeHkRB\n5fczZ3lc+RF2PKyL0S1kTqn8/hbEpffhB4nqPGwMsWHlYgzo3ATZ0it/zpSPT4axm0P9GxEswBHr\nZwxDg3q1VPsmyYg+M7ZEOgDcMa4FCtcdGuqz0/Ti4jp0bFsfRTMp/zCgQKk6vfHUJOy/DbGFASAR\nERElRgwAiShB0PkRgH52GFxbObImLY6+V78szhvDayZHirwt8Nkp1IgePwu0r5IxbADo8w1986SQ\njlUG1ww0f80+NL2Z/Av1+LU3RUVw/ogmylF2kQgAf7w9ij7duqFbpFtvHH4UdmTfL94maFY2rXRe\nGbDp1jdRDOFscAK5k0vnlrUGHkVwbh/PrEGxEpWweNMObNm8GZt/tXVoU1T53qZA9+EL5drj9xbi\nUT7YNKw2kuath69qYd/TkyuQUxlKSu/VqPXhBCFxLLEGgEHupmhfPBUUyfPhpoX698EenYoqkKF8\nP1iGvnrX5SNqawsAfxqglRwA1sNzC83LWteL4H3xIX1RUfG1eIJKylF2kQgAjR7v0PLzEFEbhItv\nQyWRaoIcX6FyHmVIVwBHX/0U1RDfH29DGumcFYVb4WM05/IJcniNOrmVf0DIhX2PQkZYKi+Nntup\nDNKV6Qh7tbDv+s6ZyJhE+bOSHHMPvRBV7SzvbUCWHCVw77v2ZP3V0flIqkiFPU9CRjc6GN5H85LZ\n5M+ifNfp+O1dEWIAA0AiIiJKjBgAElGCEF/uAXhqzQwMHDwK0+fPx8KFS/Df+u149y2ckS9xFADG\nhjen5suXOneZv09UQjzcPFU+597LL4uKdm42FjAwMNDS9DGqSgHpGKkxc/01uWb9U3Up8acr8+Rj\nj9ikbaIPNwytmBuKdGXxKQ4nRg5P4r4HYAB2zx+HwUNHY8aCBVi4eDHWbdqDzzbhXCIeRwFgbLiz\nbbT4zl4SlRAXFg6Wt43b9VhUoufgzEZQZK6M9y4h78mTQyPlY88+aiAqagKs0SFvWqQp2AiW4f3z\n4WmKJkWzYfx+zRGVwQLtnqBiOgUydpsvX7od2nH5PqHJsO1aeKN4Yw4DQCIiIkqMGAASUYKg6wGg\nr9VDlJB+uS7Xdizu3LuLu3dFu3MHd6R27/EruIS+4i/wC5qXSApFkty4Yqb2W7ffN7TMoZz1tgRu\nG2uGGsdntJB/iR+24oKoCI5vUUL65VuRvgZehh1Y9FcY39yErEnS4r8rxqIinda70yiSKQWm73sg\nKsGCYGqgjxcfzMV6RCK6BNgX20c1hyJdadw1VQtRJU6fb6JyqbLYdPOjqOiWxBoAun4+h+zS667X\nbw7uB/+cqP2sPHz2Dh6aX3vpQc9RJYf0/c5UGs/Vr4z9+Q7VlZfM6tXGG81r77F5WDX5/Z25R/PS\nez/zu8iuJz0mf2sts9X+HfrH5yN90qw4+DzkUvfv93cgV/q0WH3xvagEC4DR62d4bWQp1iWBHtg8\nvS9adB6Bsw/f/5pcx8PaBCtHdkCDrtNhE/q1Bbphcbca0MtVC2/sNG+Oaa1/EqVKVMLRF+Ffor96\nUD2Ubz9Da7gX7PWZFfIIxtlHQoWEXrYY2boqus08IL2a2McAkIiIiBIjBoBElCDEhxGAX28dw7Rp\ny3H69Okw7djhnejbrDyqdZgM10Dpl33Duzh8+AhOnj6DM2dO4+jhw7j3+iPMDPQ16keOHMbVe09g\n98MMZw4fwpHjJ6X6GZw4dgxHTp2Dq7cyHHgoP+a0VD9z5iSOSPvdeR3BJbuxzMPeBJdOHsWJM9fw\nLZypXT2sPmB428ao33YEXlv/bmKQABg+uCG9tgv4ahX+ncccTD/g4tkT0nt2FNfvPYOd598fCRkV\niXkE4LszOzFjzjqtPytH9m9Bp9pF0XjAUjnY+vL2uvz9PiV/v09J3+/DePLBGF9ePcHhI5r1W0/0\nYWNuhOPSz8BR8bNy/Jj0XTx3BV5+QXj39LZ8LGX9zOkT0vIhPPr4Q3VSccDZ8hPOH5d+di/chLWr\n9u+rk8kz9GpWD017TIaRg9qlt/5eePvsLk6dPI4DB6R/J+48g5Vr5Ga9sTZ6g3Onj0s/K8dw+5E+\nnMLejlCDr5MFLp2/gZ9ekbtRoK+rNe5cOy8dX3qvL9+GieXfu/+fEgNAIiIiSowYABJRgqDTAaCv\nHfrXzI76I9eIgnb39izAyLl7oMMT09JflBgDwEB3c7QplR4d5hwWFe3OrZuKCSvP/JXRYpTwMAAk\nIiKixIgBIBElCLo+AtD46UV0blRWDnTUW9JUaZGjYHF0H7MIX5xDX9dIiVliHQH47s4RtK5ZJMzP\nSvI06ZG7aBkMnLEOljo8ezPpPgaARERElBgxACSiBCG+TAJCFFmJexIQotjDAJCIiIgSIwaARJQg\nMACkhIYBIFHsYABIREREiREDQCJKEBgAUkLDAJAodjAAJCIiosSIASARJQgMAP9QgA/MjQ1w5cwe\njBs5Gx9tOBVJXGMAmDAEBfrA7PMHXDqxC+PGjoexvW7PPp0YMAAkIiKixIgBIBElCAwA/0QQfH19\n4W35DHWyKqBIURlPLXzFtnjO3RST+7fFuGX7YG7rAqefFjiyZjSy5yiC9RdeiZ3Cerpnkhy+hW6j\nt96R3q2/gwFgQhAEPx9feH27g8qZpO9Q5mJ4Y/O3vkF/x6XlfcP8nCjbgpNvxB4qp+Z307qfept5\n+IXYO3YxACQiIqLEiAEgESUIDABjgMtHtCws/SKeLGEEgN8f70ByhQIj1j0QlRD2Lw4ig7StUPPJ\ncAuVx9zdNRUD5+8Xa3GHAWAC4vQcdQpJP1sZElYAeGbVCIxdf1mshe/Nzb1YvudcuOH5+qF1Ua3n\nUvytedAZABIREVFixACQiBKEuAoArT7cxbDOTVEkfz6Urt4AoydPw4QhC/H4g7PYQykQt45tQMfm\ntVFY2i9n7vxo3HEgjt99L7YD/j7ueHXvItYsnIv2DSph0q4bUjUQF7bPR7USOZE2Y250H7cSFq6q\n/a3f38Xgjo2QLnVKlKzeArtvvlNtkHg5W+Pq8X2YO2UsalevgnOGzvhpeB/9WtVF6uQpUbRqI6zc\nfx1hLkT8TQD4491tTBzRH9XKFkCOLNlQq11fHL2hL7YGC8JD6bW2qVsB+fLlQ/2WXTBj1mxMG78X\njhFd+ejvhEOrF2PGjBmRbhv3nYSfeHhY/pjRpaZqVNEZA1FT54GJ9YvL2xceCH4NQbi/c/yv0UiK\nZKlRslZrrD96E3FxQXRiDwC/v7mBgR2aoJD0PSpXsyFGT5mJyaNnQt9c/WcLeHXjCPp0boISBfIh\nd45c+L+9+4COotzfOL40QZqACJcOilIE5X+pFxX00pEiVQEpQihemoAUAelFpRmkS5civUoR8NKr\nIYQivSWQAgECCUmAhOe/Ozsh2ZDQJMCdfD/nvOewv3lnd3Z23pwzD+/MlKpQT+MW/FcR5vLI2+E6\numejxo3or0/tY6BhmzlGfcviCapY/A29nPYVVWvcTV6+wUY9xOeAerf8WK+mT6msBUrruxmb7wVX\nt0Kvauuq+Rr6bTd9+M/SGvXraYUHHlT3z6sofaoUyvJWKX0zco6uRn14lIcEgOf/XK+urRqreMHX\n9VqmDHr/k6ZatDH670OUgxtnq07lMspl3yclPvxYXXr0Vtf+/XUs6AHRWfglzRncL84xFF8bO2/1\n/X8fXERq6bDG0WMlZRq9U76Ofl6+Jd6QLz77pndR+pwldcj82/YsEAACAIDEiAAQgCU8jwBw7egm\nSpm2gi6br52uq0etL7RgW4DxKjJwnwqnt58gZyioMzHSqglulYwT52YjNpgVp8k9Shj1krVaa8O2\nv+6FGKc3TVJmx4l2przqNXqiDvtGnS1HalCzUsY6bdxXmjWHYLX4MKtsSZOrUvPe2nf6ilmXlo/+\nUimN93pbW06FmFW7eALAO1cO6v3c6eU2Zp1ZcfJcNNiYYZcidzkdMQKIULUqnU2lG/7s7GC6eWKt\nGlXpqQvP+NZnO6d1M/bLG1U72rcstmtqUSavsbz7uN/MWgx3QnXu0HYN7FBfrySx7xN7v/JtvtPN\nZzh5KzEHgEu+r6s0OSrohvnaKUg9Gn6ulR7miIvwV6PXk9n3US5tOBf9w2xwb27st8odR5sVp/Vj\nOhj1fO/U0+KtO+/9ljeOr1HhzElkS2Y/xr8Zq11/+TkX2P3Sq6Hzt2872aw4jarjCJeTqkSVFtp8\n8LxZlTyWDlXmZI7jJbNm7Y6uxxcA3rrsoX/leEUdJ2w1K067ZvXVS/bPTZ2voo4FO/sP/6yksv37\nS+Pf9wQdUZvmDbQv4BkemLGFh+iEx0Z1b1FNqezb7NhftXv9rEeZQ3zHb4cKZ8yo0RvPmJVngwAQ\nAAAkRgSAACzheQSAJ/8Yp9SOE9402dR50Dgd9Qkyl7i6dO6Ezgc655AFnD6krRtWqOvHzqCvSfdV\nRj3KvGGfG/VWPy43K6abJ1WroOPkuqi2nnWdj7Z60jfGOqX6xAzerqlVpTz2egr9stfXrEVb8EMz\nY53ibceYFbs4A8AIDWlaWLbM+dSt30D1798/uvXqqoZVq6pq1Zr6edNJo/eUXtWM981SpIJ+nLde\nV8KM8sNFhGjb6mWaP3/+I7d1W3bfC0jjE3rRU53rl1PenAXV8/tJ+m3TFu3ZvlHD2jdUcvt2JslS\nXLv8459HGGXeoM+M71W172yzkvAScwB4fP0EI0xKmi6Xug2dqBO+cY+t65fO6rR3oPHvG74ntWPT\nWg1uX8PYb+XaDTPqUfZP7GPUP2g4xaxECVbjDwvYl6WW+74LZs3p1O+jjJC7SNXeinkoj69bxniv\n/lPun6XnMWugktqX5f+gdXSAGWcAeEu9GxaRLVshdR0wwGVs9fvmK9Wr6RhbdTR33Vmj9+bxXxmf\nmSb7PzVowkL5XHv4cWu4fUPbly6KcwzF19bt8Hzo2IrP2M7O/9z4fEwcwbqLUP2nwpsq33qC+frZ\nIQAEAACJEQEgAEt4nvcAPHvwD33dsobSJ3XOfslVsrZ2nXdeTujgvfNXlXu7kLqPX6GbZm1ND+fl\nc41jBYBzhzrrX4xealZMISdU8y3H+xfR5jOuqdqqiT2Ndcr0nWpWHKIDwJm7XEMNh4OLRhrrFGg5\nIvqSvTgDwBB1r5hJttQFtfn8o07hC9XWZRNUt5wjVHHuk487jlTQkyYKT1vgfpXInky2ZHm1/IBz\npubDXdYn+dKrZPvx5uuExz0ApVP7N6hLs+pKax5HecrW117v6PmcQae2qH6Zd/V576m6bObiHov7\nG30/aOsaAHpM6G3U328wyaxEuaFG5R2Xg6fWj3t8zJrTyXUjjQCwaNU+cQaAfSe5PujC4ZLXZGW0\nL8tb+lP7KDTFGQBeUbsqSWRLWUJ7Hvm+gHfl9ccCfVH7X0bI6NiGkrW6yDv4RRlcdmGn9d6rL6na\nwIVmIW6bxrRSlkLVdPFRpgo+ZQSAAAAgMSIABGAJzyMAPHt0hwLCXU+8Ix2X9GVKolSFK8gRAW6b\n6py18+moxc4OpvW9nTPwmvRwnSUzf3hTo95yzDKzYgo5qZoFHCf8RbUl1gzAVRN7GeuU6TfNrDhE\nBYDJNftPf7MWbW7/mvZlL2v2jhjh4PUjqvq6/TOSF9dun+jZRb/92NZ4/3T5ymnzqftv1LVr+WQt\n2n5KigjW4b/23nfvMK8lw42ZktV7TTQrz8+m8c6n+1brONL4fR7V0TWjVKFRZ/k/6ozGpyAxB4Cn\nj+6Vf6zrtu/471KJzDYlLVFPjqPQe60zxC75yShnB5PXisFGvfyXw82Kk+fEvkb9/Qaul/M6ZgA2\nKv+WfVlque+NOwB8p9q3LveBvDcDcOr9MwB3Te1uLOs+38Os2F3bo7KOcD39mzoQYzguG+5m9H3l\njYradi7qvweibV0yTcu3n7P/65b2ex1VaKyc7/KfvypTCpvy/XvAQ+7Z9+zsnNNXVd366cYDMsng\nYyuVL1N2zdkffWuCZ4kAEAAAJEYEgAAs4XkEgOvG17KfvCdV857jdeCUt3x8vLXgBzelTZVd7uuO\nGX389i5SzuTOmTrFKzeQW9sWat9nsAa0+dioZcxXVC26fKuNB4/o+P5NqpI/iVF/tWRDbdl/XNdD\nb8nf54zWTOuvl+11x7LPB0zVX2cvKjQsSCcP7VCrj5wPs0ias7xWbzuoqzcdUUCQ3Krlt9eTKXfB\n0ho5a6W8fXx02muLWv+7gFLnek/bzkZHYNcunNOKid/cm1XUpP80HTnjo9vmxCSPlaOUy3EvQ3N5\nVEua/k3N2R0VmtxU6wr27c9QSO6zVxuf53PcQx2rF1amt2vpYIBrcJnQQq/76vDB/Vryy2R92bSO\nylaorTkboh+WEtuJjaOUxvxe+YuVV9v/dFafnr3U+zt37T/neje6ZyExB4DLf3DMhE2plv0my8sc\nW7MHtlCalDk1aaPzcvO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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "image/png": {
       "height": 1000,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"tree_cabin.png\"\n",
    "Image(filename = PATH , width=1000, height=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the plot, we only obtain 3 bins, because the first split resulted already in a final node on the left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Cabin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin_tree</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.303609</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.600000</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.736842</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cabin  Cabin\n",
       "Cabin_tree              \n",
       "0.303609        0      0\n",
       "0.600000        1      3\n",
       "0.736842        4      5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see what are the Fare cut-offs within each bin\n",
    "pd.concat( [X_train.groupby(['Cabin_tree'])['Cabin'].min(),\n",
    "            X_train.groupby(['Cabin_tree'])['Cabin'].max()], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision tree has found these buckets as the optimal ones: Cabins 0, 1-3 and 4-5, with probability of survival .3, .6 and .73 respectively.\n",
    "\n",
    "Amazing, no?\n",
    "\n",
    "**That is all for this demonstration. I hope you enjoyed the notebook, and see you in the next one.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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